Point cloud compression with adaptive filtering

ABSTRACT

A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. A processing/filtering element utilizes occupancy map information and/or auxiliary patch information to determine relationships between patches in image frames and adjusts encoding/decoding and/or filtering or pre/post-processing parameters based on the determined relationships.

PRIORITY CLAIM

This application claims benefit of priority to U.S. ProvisionalApplication Ser. No. 62/693,376, entitled “Point Cloud Compression withAdaptive Filtering”, filed Jul. 2, 2018, and which is incorporatedherein by reference in its entirety.

BACKGROUND Technical Field

This disclosure relates generally to compression and decompression ofpoint clouds comprising a plurality of points, each having associatedspatial information and attribute information.

Description of the Related Art

Various types of sensors, such as light detection and ranging (LIDAR)systems, 3-D-cameras, 3-D scanners, etc. may capture data indicatingpositions of points in three dimensional space, for example positions inthe X, Y, and Z planes. Also, such systems may further capture attributeinformation in addition to spatial information for the respectivepoints, such as color information (e.g. RGB values), textureinformation, intensity attributes, reflectivity attributes, motionrelated attributes, modality attributes, or various other attributes. Insome circumstances, additional attributes may be assigned to therespective points, such as a time-stamp when the point was captured.Points captured by such sensors may make up a “point cloud” comprising aset of points each having associated spatial information and one or moreassociated attributes. In some circumstances, a point cloud may includethousands of points, hundreds of thousands of points, millions ofpoints, or even more points. Also, in some circumstances, point cloudsmay be generated, for example in software, as opposed to being capturedby one or more sensors. In either case, such point clouds may includelarge amounts of data and may be costly and time-consuming to store andtransmit.

SUMMARY OF EMBODIMENTS

In some embodiments, a system includes one or more sensors configured tocapture points that collectively make up a point cloud, wherein each ofthe points comprises spatial information identifying a spatial locationof the respective point and attribute information defining one or moreattributes associated with the respective point.

The system also includes an encoder configured to compress the attributeand/or spatial information of the points. To compress the attributeand/or spatial information, the encoder is configured to determine, forthe point cloud, a plurality of patches, each corresponding to portionsof the point cloud. The encoder is further configured to, for eachpatch, generate a patch image comprising the set of points correspondingto the patch projected onto a patch plane and generate another patchimage comprising depth information for the set of points correspondingto the patch, wherein the depth information represents depths of thepoints in a direction perpendicular to the patch plane.

For example, the patch image corresponding to the patch projected onto apatch plane may depict the points of the point cloud included in thepatch in two directions, such as an X and Y direction. The points of thepoint cloud may be projected onto a patch plane approximatelyperpendicular to a normal vector, normal to a surface of the point cloudat the location of the patch. Also, for example, the patch imagecomprising depth information for the set of points included in the patchmay depict depth information, such as depth distances in a Z direction.To depict the depth information, the depth patch image may include aparameter that varies in intensity based on the depth of points in thepoint cloud at a particular location in the patch image. For example,the patch image depicting depth information may have a same shape as thepatch image representing attributes of points projected onto the patchplane. However, the depth information patch image may be an imagecomprising image attributes, such as one or more colors, that vary inintensity based on depth, wherein the intensity of the one or more imageattributes corresponds to a depth of a corresponding point of the pointcloud at a location in the patch image where the image attribute isdisplayed in the patch image depicting depth. For example, points thatare closer to the patch plane may be encoded as darker values in thepatch image depicting depth and points that are further away from thepatch plane may be encoded as lighter values in the patch imagedepicting depth, for example in a monochromatic patch image depictingdepth. Thus, the depth information patch image when aligned with otherpatch images representing attribute values for points projected onto thepatch plane may indicate the relative depths of the points projectedonto the patch plane, based on respective image attribute intensities atlocations in the depth patch image that correspond to locations of thepoints in the other patch images comprising point cloud points projectedonto the patch plane.

The encoder is further configured to pack generated patch images(including a depth patch image and one or more additional patch imagesfor one or more other attributes) for each of the determined patchesinto one or more image frames. Also, the encoder is configured toprovide the one or more packed image frames to a video encodingcomponent (which may be included in the encoder or may be a separatevideo encoding component). Additionally, the encoder is configured toprovide to the video encoding component relationship informationindicating relationships between the respective attribute patch images,depth patch images, and/or image frames. For example, the relationshipinformation, may indicate portions of the image frames that are occupiedor unoccupied with patch images, patch images that correspond to a sameset of points projected on a same patch plane, patch images comprisingpoints with similar or the same depths in the point cloud, patch imagescomprising points having similar or the same attributes, or variousother relationships as described herein. The video encoding component isconfigured to adjust one or more parameters used to video encode theimage frames based, at least in part, on the provided relationshipinformation. In some embodiments, the video encoding component mayutilize various image or video encoding techniques to encode the one ormore image frames and adjust parameters of the encoding based on theprovided relationship information. For example, the encoder may utilizea video encoder in accordance with the High Efficiency Video Coding(HEVC/H.265) standard or other suitable standards such as, the AdvancedVideo Coding (AVC/H.264) standard, the AOMedia Video 1 (AV1) videocoding format produced by the Alliance for Open Media (AOM), etc. Insome embodiments, the encoder may utilize an image encoder in accordancewith a Motion Picture Experts Group (MPEG), a Joint Photography ExpertsGroup (JPEG) standard, an International TelecommunicationUnion-Telecommunication standard (e.g. ITU-T standard), etc.

In some embodiments, a decoder is configured to receive one or moreencoded image frames comprising patch images for a plurality of patchesof a compressed point cloud, wherein, for each patch, the one or moreencoded image frames comprise: a patch image comprising a set of pointsof the patch projected onto a patch plane and a patch image comprisingdepth information for the set of points of the patch, wherein the depthinformation indicates depths of the points of the patch in a directionperpendicular to the patch plane. In some embodiments, a depth patchimage may be packed into an image frame with other attribute patchimages. For example, a decoder may receive one or more image framescomprising packed patch images as generated by the encoder describedabove. The decoder also receives an occupancy map for the one or moreencoded image frames.

The decoder is further configured to video decode the one or more videoencoded image frames comprising the patch images. In some embodiments,the decoder may utilize a video decoder in accordance with the HighEfficiency Video Coding (HEVC) standard or other suitable standards suchas, the Advanced Video Coding (AVC) standard, the AOMedia Video 1 (AV1)video coding format, etc. In some embodiments, the decoder may utilizean image decoder in accordance with a Motion Picture Experts Group(MPEG) or a Joint Photography Experts Group (JPEG) standard, etc.

The decoder is further configured to receive or determine relationshipinformation indicating relationships between the respective attributepatch images, depth patch images, and/or image frames. For example, thedecoder may receive relationship information in a compressed point cloudfile, wherein the relationship information was determined by an encoder.Also, in some embodiments, the decoder may determine relationshipinformation based on information included in a compressed point cloudfile, such as an occupancy map and/or auxiliary information for thecompressed point cloud. In some embodiments, the decoder may utilize thereceived or determined relationship information to adjust one or moreparameters used to video decode the video encoded image frames.

Additionally, the decoder is configured to perform one or morepost-processing processes taking into account the received or determinedrelationship information. For example, the decoder may performdenoising, debanding, derining, deblocking, or sharpening of the videodecoded image frames. Also, the decoder may perform an object extractionor segmentation process, display mapping process, color spaceconversion, filtering, color adjustment, or tone adjustment, taking intoaccount the received or determined relationship information. Forexample, patches representing similar sets of points may be filtered ina way such that unrelated patches are not considered by the filter.Also, unoccupied padding in the one or more image frames may be excludedfrom consideration by the filter. Various other adjustments topost-processing processed based on received or determine relationshipinformation may be performed by a decoder as described herein.

The decoder is further configured to determine, for each patch, spatialinformation for the set of points of the patch based, at least in part,on the patch image comprising the set of points of the patch projectedonto the patch plane and the patch image comprising the depthinformation for the set of points of the patch, and generate areconstructed version of the compressed point cloud based, at least inpart, on the determined spatial information for the plurality of patchesand the attribute information included in the patches.

In some embodiments, a method includes receiving one or more encodedimage frames comprising patch images for a plurality of patches of acompressed point cloud, wherein, for each patch, the one or more encodedimage frames comprise: a patch image comprising a set of points of thepatch projected onto a patch plane and a patch image comprising depthinformation for the set of points of the patch, wherein the depthinformation indicates depths of the points of the patch in a directionperpendicular to the patch plane. The method further includes receivingan occupancy map for the one or more image frames. The method furtherincludes decoding the one or more encoded image frames comprising thepatch images. In some embodiments, decoding may be performed inaccordance with the High Efficiency Video Coding (HEVC) standard orother suitable standards such as, the Advanced Video Coding (AVC)standard, an AOMedia Video 1 (AV1) video coding format, etc. In someembodiments, decoding may be performed in accordance with a MotionPicture Experts Group (MPEG) or a Joint Photography Experts Group (JPEG)standard, etc.

The method further includes performing one or more post-processingprocesses on the one or more image frames. Performing the one or morepost-processing processes may include determining or receivingrelationship information indicating relationships between the respectiveattribute patch images and the respective depth patch images, whereinthe relationship information is determined based on the occupancy map,the attribute patch images, or the depth patch images. Also, performingthe one or more post-processing processes may include adjusting one ormore parameters of the one or more post processing processes based onthe determined or received relationship information.

The method further includes determining, for each patch, spatialinformation for the set of points of the patch based, at least in part,on the patch image comprising the set of points of the patch projectedonto the patch plane and the patch image comprising the depthinformation for the set of points of the patch, and generating areconstructed representation of the compressed point cloud based, atleast in part, on the determined spatial information for the pluralityof patches.

In some embodiments, a non-transitory computer-readable medium storesprogram instructions that, when executed by one or more processors,cause the one or more processors to implement an encoder as describedherein to compress attribute information of a point cloud.

In some embodiments, a non-transitory computer-readable medium storesprogram instructions that, when executed by one or more processors,cause the one or more processors to implement a decoder as describedherein to decompress attribute information of a point cloud.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a system comprising a sensor that capturesinformation for points of a point cloud and an encoder that compressesspatial information and attribute information of the point cloud, wherethe compressed spatial and attribute information is sent to a decoder,according to some embodiments.

FIG. 2A illustrates components of an encoder for encoding intra pointcloud frames, according to some embodiments.

FIG. 2B illustrates components of a decoder for decoding intra pointcloud frames, according to some embodiments.

FIG. 2C illustrates components of an encoder for encoding inter pointcloud frames, according to some embodiments.

FIG. 2D illustrates components of a decoder for decoding inter pointcloud frames, according to some embodiments.

FIG. 3A illustrates an example patch segmentation process, according tosome embodiments.

FIG. 3B illustrates an example image frame comprising packed patchimages and padded portions, according to some embodiments.

FIG. 3C illustrates an example image frame comprising patch portions andpadded portions, according to some embodiments.

FIG. 3D illustrates a point cloud being projected onto multipleprojections, according to some embodiments.

FIG. 3E illustrates a point cloud being projected onto multiple parallelprojections, according to some embodiments.

FIG. 4A illustrates components of an encoder for encoding intra pointcloud frames with color conversion, according to some embodiments.

FIG. 4B illustrates components of an encoder for encoding inter pointcloud frames with color conversion, according to some embodiments.

FIG. 4C illustrates components of a closed-loop color conversion module,according to some embodiments.

FIG. 4D illustrates an example process for determining a quality metricfor a point cloud upon which an operation has been performed, accordingto some embodiments.

FIG. 5A illustrates components of an encoder that includes geometry,texture, and/or other attribute downscaling, according to someembodiments.

FIG. 5B illustrates components of a decoder that includes geometry,texture, and/or other attribute upscaling, according to someembodiments.

FIG. 5C illustrates rescaling from the perspective of an encoder,according to some embodiments.

FIG. 5D illustrates rescaling from the perspective of a decoder,according to some embodiments.

FIG. 5E illustrates an example open loop rescaling, according to someembodiments.

FIG. 5F illustrates an example closed loop rescaling, according to someembodiments.

FIG. 5G illustrates an example closed loop rescaling with multipleattribute layers, according to some embodiments.

FIG. 5H illustrates an example of video level spatiotemporal scaling,according to some embodiments.

FIG. 5I illustrates an example closed loop rescaling with spatiotemporalscaling, according to some embodiments.

FIG. 6A illustrates components of an encoder that further includespre-video compression texture processing and/or filtering and pre videocompression geometry processing/filtering, according to someembodiments.

FIG. 6B illustrates components of a decoder that further includes postvideo decompression texture processing and/or filtering and post videodecompression geometry processing/filtering, according to someembodiments.

FIG. 6C illustrates, a bit stream structure for a compressed pointcloud, according to some embodiments.

FIG. 6D illustrates a process for generating video encoded image framesfor patches of a point cloud taking into account relationshipinformation between the patches packed into the image frames, accordingto some embodiments.

FIG. 6E illustrates a process for generating video encoded image framestaking into account pooled distortion for a set of patches correspondingto a same set of points, according to some embodiments.

FIG. 6F illustrates a process for generating video encoded image framestaking into account patch edges, according to some embodiments.

FIG. 6G illustrates a process for reconstructing a point cloud based onvideo encoded image frames comprising patches of the point cloud,wherein relationship information between the patches packed into theimage frames is taken into account, according to some embodiments.

FIG. 6H illustrates a process of upscaling a patch image included in animage frame taking into account edges of the patch image determinedbased on received or determined relationship information for thepatches, according to some embodiments.

FIG. 6I illustrates an example application where an attribute plane isup-scaled using its corresponding geometry information and the geometryextracted edges, according to some embodiments.

FIG. 7A illustrates an example of a PCCNAL unit based bit stream,according to some embodiments.

FIG. 7B illustrates an example of a PCCNAL units grouped by POC,according to some embodiments.

FIG. 7C illustrates an example of a PCCNAL unit grouped by type,according to some embodiments.

FIG. 8A illustrates a process for compressing attribute and spatialinformation of a point cloud, according to some embodiments.

FIG. 8B illustrates a process for decompressing attribute and spatialinformation of a point cloud, according to some embodiments.

FIG. 8C illustrates patch images being generated and packed into animage frame to compress attribute and spatial information of a pointcloud, according to some embodiments.

FIG. 9 illustrates patch images being generated and packed into an imageframe to compress attribute and spatial information of a moving orchanging point cloud, according to some embodiments.

FIG. 10 illustrates a decoder receiving image frames comprising patchimages, patch information, and an occupancy map, and generating adecompressed representation of a point cloud, according to someembodiments.

FIG. 11A illustrates an encoder, adjusting encoding based on one or moremasks for a point cloud, according to some embodiments.

FIG. 11B illustrates a decoder, adjusting decoding based on one or moremasks for a point cloud, according to some embodiments.

FIG. 12A illustrates more detail regarding compression of an occupancymap, according to some embodiments.

FIG. 12B illustrates example blocks and traversal patterns forcompressing an occupancy map, according to some embodiments.

FIG. 12C illustrates more detail regarding compression of an occupancymap, according to some embodiments.

FIG. 13 illustrates compressed point cloud information being used in a3-D telepresence application, according to some embodiments.

FIG. 14 illustrates compressed point cloud information being used in avirtual reality application, according to some embodiments.

FIG. 15 illustrates an example computer system that may implement anencoder or decoder, according to some embodiments.

This specification includes references to “one embodiment” or “anembodiment.” The appearances of the phrases “in one embodiment” or “inan embodiment” do not necessarily refer to the same embodiment.Particular features, structures, or characteristics may be combined inany suitable manner consistent with this disclosure.

“Comprising.” This term is open-ended. As used in the appended claims,this term does not foreclose additional structure or steps. Consider aclaim that recites: “An apparatus comprising one or more processor units. . . .” Such a claim does not foreclose the apparatus from includingadditional components (e.g., a network interface unit, graphicscircuitry, etc.).

“Configured To.” Various units, circuits, or other components may bedescribed or claimed as “configured to” perform a task or tasks. In suchcontexts, “configured to” is used to connote structure by indicatingthat the units/circuits/components include structure (e.g., circuitry)that performs those task or tasks during operation. As such, theunit/circuit/component can be said to be configured to perform the taskeven when the specified unit/circuit/component is not currentlyoperational (e.g., is not on). The units/circuits/components used withthe “configured to” language include hardware—for example, circuits,memory storing program instructions executable to implement theoperation, etc. Reciting that a unit/circuit/component is “configuredto” perform one or more tasks is expressly intended not to invoke 35U.S.C. § 112(f), for that unit/circuit/component. Additionally,“configured to” can include generic structure (e.g., generic circuitry)that is manipulated by software and/or firmware (e.g., an FPGA or ageneral-purpose processor executing software) to operate in manner thatis capable of performing the task(s) at issue. “Configure to” may alsoinclude adapting a manufacturing process (e.g., a semiconductorfabrication facility) to fabricate devices (e.g., integrated circuits)that are adapted to implement or perform one or more tasks.

“First,” “Second,” etc. As used herein, these terms are used as labelsfor nouns that they precede, and do not imply any type of ordering(e.g., spatial, temporal, logical, etc.). For example, a buffer circuitmay be described herein as performing write operations for “first” and“second” values. The terms “first” and “second” do not necessarily implythat the first value must be written before the second value.

“Based On.” As used herein, this term is used to describe one or morefactors that affect a determination. This term does not forecloseadditional factors that may affect a determination. That is, adetermination may be solely based on those factors or based, at least inpart, on those factors. Consider the phrase “determine A based on B.”While in this case, B is a factor that affects the determination of A,such a phrase does not foreclose the determination of A from also beingbased on C. In other instances, A may be determined based solely on B.

DETAILED DESCRIPTION

As data acquisition and display technologies have become more advanced,the ability to capture point clouds comprising thousands or millions ofpoints in 2-D or 3-D space, such as via LIDAR systems, has increased.Also, the development of advanced display technologies, such as virtualreality or augmented reality systems, has increased potential uses forpoint clouds. However, point cloud files are often very large and may becostly and time-consuming to store and transmit. For example,communication of point clouds over private or public networks, such asthe Internet, may require considerable amounts of time and/or networkresources, such that some uses of point cloud data, such as real-timeuses, may be limited. Also, storage requirements of point cloud filesmay consume a significant amount of storage capacity of devices storingthe point cloud files, which may also limit potential applications forusing point cloud data.

In some embodiments, an encoder may be used to generate a compressedpoint cloud to reduce costs and time associated with storing andtransmitting large point cloud files. In some embodiments, a system mayinclude an encoder that compresses attribute and/or spatial informationof a point cloud file such that the point cloud file may be stored andtransmitted more quickly than non-compressed point clouds and in amanner that the point cloud file may occupy less storage space thannon-compressed point clouds. In some embodiments, compression ofattributes of points in a point cloud may enable a point cloud to becommunicated over a network in real-time or in near real-time. Forexample, a system may include a sensor that captures attributeinformation about points in an environment where the sensor is located,wherein the captured points and corresponding attributes make up a pointcloud. The system may also include an encoder that compresses thecaptured point cloud attribute information. The compressed attributeinformation of the point cloud may be sent over a network in real-timeor near real-time to a decoder that decompresses the compressedattribute information of the point cloud. The decompressed point cloudmay be further processed, for example to make a control decision basedon the surrounding environment at the location of the sensor. Thecontrol decision may then be communicated back to a device at or nearthe location of the sensor, wherein the device receiving the controldecision implements the control decision in real-time or near real-time.In some embodiments, the decoder may be associated with an augmentedreality system and the decompressed attribute information may bedisplayed or otherwise used by the augmented reality system. In someembodiments, compressed attribute information for a point cloud may besent with compressed spatial information for points of the point cloud.In other embodiments, spatial information and attribute information maybe separately encoded and/or separately transmitted to a decoder.

In some embodiments, a system may include a decoder that receives one ormore sets of point cloud data comprising compressed attributeinformation via a network from a remote server or other storage devicethat stores the one or more point cloud files. For example, a 3-Ddisplay, a holographic display, or a head-mounted display may bemanipulated in real-time or near real-time to show different portions ofa virtual world represented by point clouds. In order to update the 3-Ddisplay, the holographic display, or the head-mounted display, a systemassociated with the decoder may request point cloud data from the remoteserver based on user manipulations of the displays, and the point clouddata may be transmitted from the remote server to the decoder anddecoded by the decoder in real-time or near real-time. The displays maythen be updated with updated point cloud data responsive to the usermanipulations, such as updated point attributes.

In some embodiments, a system, may include one or more LIDAR systems,3-D cameras, 3-D scanners, etc., and such sensor devices may capturespatial information, such as X, Y, and Z coordinates for points in aview of the sensor devices. In some embodiments, the spatial informationmay be relative to a local coordinate system or may be relative to aglobal coordinate system (for example, a Cartesian coordinate system mayhave a fixed reference point, such as a fixed point on the earth, or mayhave a non-fixed local reference point, such as a sensor location).

In some embodiments, such sensors may also capture attribute informationfor one or more points, such as color attributes, reflectivityattributes, velocity attributes, acceleration attributes, timeattributes, modalities, and/or various other attributes. In someembodiments, other sensors, in addition to LIDAR systems, 3-D cameras,3-D scanners, etc., may capture attribute information to be included ina point cloud. For example, in some embodiments, a gyroscope oraccelerometer, may capture motion information to be included in a pointcloud as an attribute associated with one or more points of the pointcloud. For example, a vehicle equipped with a LIDAR system, a 3-Dcamera, or a 3-D scanner may include the vehicle's direction and speedin a point cloud captured by the LIDAR system, the 3-D camera, or the3-D scanner. For example, when points in a view of the vehicle arecaptured they may be included in a point cloud, wherein the point cloudincludes the captured points and associated motion informationcorresponding to a state of the vehicle when the points were captured.

Example System Arrangement

FIG. 1 illustrates a system comprising a sensor that capturesinformation for points of a point cloud and an encoder that compressesattribute information of the point cloud, where the compressed attributeinformation is sent to a decoder, according to some embodiments.

System 100 includes sensor 102 and encoder 104. Sensor 102 captures apoint cloud 110 comprising points representing structure 106 in view 108of sensor 102. For example, in some embodiments, structure 106 may be amountain range, a building, a sign, an environment surrounding a street,or any other type of structure. In some embodiments, a captured pointcloud, such as captured point cloud 110, may include spatial andattribute information for the points included in the point cloud. Forexample, point A of captured point cloud 110 comprises X, Y, Zcoordinates and attributes 1, 2, and 3. In some embodiments, attributesof a point may include attributes such as R, G, B color values, avelocity at the point, an acceleration at the point, a reflectance ofthe structure at the point, a time stamp indicating when the point wascaptured, a string-value indicating a modality when the point wascaptured, for example “walking”, or other attributes. The captured pointcloud 110 may be provided to encoder 104, wherein encoder 104 generatesa compressed version of the point cloud (compressed attributeinformation 112) that is transmitted via network 114 to decoder 116. Insome embodiments, a compressed version of the point cloud, such ascompressed attribute information 112, may be included in a commoncompressed point cloud that also includes compressed spatial informationfor the points of the point cloud or, in some embodiments, compressedspatial information and compressed attribute information may becommunicated as separate sets of data.

In some embodiments, encoder 104 may be integrated with sensor 102. Forexample, encoder 104 may be implemented in hardware or software includedin a sensor device, such as sensor 102. In other embodiments, encoder104 may be implemented on a separate computing device that is proximateto sensor 102.

Example Intra-Frame Encoder

FIG. 2A illustrates components of an encoder for encoding intra pointcloud frames, according to some embodiments. In some embodiments, theencoder described above in regard to FIG. 1 may operate in a similarmanner as encoder 200 described in FIG. 2A and encoder 250 described inFIG. 2C.

The encoder 200 receives uncompressed point cloud 202 and generatescompressed point cloud information 204. In some embodiments, an encoder,such as encoder 200, may receive the uncompressed point cloud 202 from asensor, such as sensor 102 illustrated in FIG. 1, or, in someembodiments, may receive the uncompressed point cloud 202 from anothersource, such as a graphics generation component that generates theuncompressed point cloud in software, as an example.

In some embodiments, an encoder, such as encoder 200, includesdecomposition into patches module 206, packing module 208, spatial imagegeneration module 210, texture image generation module 212, andattribute information generation module 214. In some embodiments, anencoder, such as encoder 200, also includes image frame padding module216, video compression module 218 and multiplexer 224. In addition, insome embodiments an encoder, such as encoder 200, may include anoccupancy map compression module, such as occupancy map compressionmodule 220, and an auxiliary patch information compression module, suchas auxiliary patch information compression module 222. In someembodiments, an encoder, such as encoder 200, converts a 3D point cloudinto an image-based representation along with some meta data (e.g.,occupancy map and patch info) necessary to convert the compressed pointcloud back into a decompressed point cloud.

In some embodiments, the conversion process decomposes the point cloudinto a set of patches (e.g., a patch is defined as a contiguous subsetof the surface described by the point cloud), which may be overlappingor not, such that each patch may be described by a depth field withrespect to a plane in 2D space. More details about the patchdecomposition process are provided above with regard to FIGS. 3A-3C.

After or in conjunction with the patches being determined for the pointcloud being compressed, a 2D sampling process is performed in planesassociated with the patches. The 2D sampling process may be applied inorder to approximate each patch with a uniformly sampled point cloud,which may be stored as a set of 2D patch images describing thegeometry/texture/attributes of the point cloud at the patch location.The “Packing” module 208 may store the 2D patch images associated withthe patches in a single (or multiple) 2D images, referred to herein as“image frames” or “video image frames.” In some embodiments, a packingmodule, such as packing module 208, may pack the 2D patch images suchthat the packed 2D patch images do not overlap (even though an outerbounding box for one patch image may overlap an outer bounding box foranother patch image). Also, the packing module may pack the 2D patchimages in a way that minimizes non-used images pixels of the imageframe.

In some embodiments, “Geometry/Texture/Attribute generation” modules,such as modules 210, 212, and 214, generate 2D patch images associatedwith the geometry/texture/attributes, respectively, of the point cloudat a given patch location. As noted before, a packing process, such asperformed by packing module 208, may leave some empty spaces between 2Dpatch images packed in an image frame. Also, a padding module, such asimage frame padding module 216, may fill in such areas in order togenerate an image frame that may be suited for 2D video and imagecodecs.

In some embodiments, an occupancy map (e.g., binary informationdescribing for each pixel or block of pixels whether the pixel or blockof pixels are padded or not) may be generated and compressed, forexample by occupancy map compression module 220. The occupancy map maybe sent to a decoder to enable the decoder to distinguish between paddedand non-padded pixels of an image frame.

Note that other metadata associated with patches may also be sent to adecoder for use in the decompression process. For example, patchinformation indicating sizes and shapes of patches determined for thepoint cloud and packed in an image frame may be generated and/or encodedby an auxiliary patch-information compression module, such as auxiliarypatch-information compression module 222. In some embodiments one ormore image frames may be encoded by a video encoder, such as videocompression module 218. In some embodiments, a video encoder, such asvideo compression module 218, may operate in accordance with the HighEfficiency Video Coding (HEVC) standard or other suitable video encodingstandard. In some embodiments, encoded video images, encoded occupancymap information, and encoded auxiliary patch information may bemultiplexed by a multiplexer, such as multiplexer 224, and provided to arecipient as compressed point cloud information, such as compressedpoint cloud information 204.

In some embodiments, an occupancy map may be encoded and decoded by avideo compression module, such as video compression module 218. This maybe done at an encoder, such as encoder 200, such that the encoder has anaccurate representation of what the occupancy map will look like whendecoded by a decoder. Also, variations in image frames due to lossycompression and decompression may be accounted for by an occupancy mapcompression module, such as occupancy map compression module 220, whendetermining an occupancy map for an image frame. In some embodiments,various techniques may be used to further compress an occupancy map,such as described in FIGS. 12A-12B.

Example Intra-Frame Decoder

FIG. 2B illustrates components of a decoder for decoding intra pointcloud frames, according to some embodiments. Decoder 230 receivescompressed point cloud information 204, which may be the same compressedpoint cloud information 204 generated by encoder 200. Decoder 230generates reconstructed point cloud 246 based on receiving thecompressed point cloud information 204.

In some embodiments, a decoder, such as decoder 230, includes ade-multiplexer 232, a video decompression module 234, an occupancy mapdecompression module 236, and an auxiliary patch-informationdecompression module 238. Additionally a decoder, such as decoder 230includes a point cloud generation module 240, which reconstructs a pointcloud based on patch images included in one or more image framesincluded in the received compressed point cloud information, such ascompressed point cloud information 204. In some embodiments, a decoder,such as decoder 203, further comprises a smoothing filter, such assmoothing filter 244. In some embodiments, a smoothing filter may smoothincongruences at edges of patches, wherein data included in patch imagesfor the patches has been used by the point cloud generation module torecreate a point cloud from the patch images for the patches. In someembodiments, a smoothing filter may be applied to the pixels located onthe patch boundaries to alleviate the distortions that may be caused bythe compression/decompression process.

Example Inter-Frame Encoder

FIG. 2C illustrates components of an encoder for encoding inter pointcloud frames, according to some embodiments. An inter point cloudencoder, such as inter point cloud encoder 250, may encode an imageframe, while considering one or more previously encoded/decoded imageframes as references.

In some embodiments, an encoder for inter point cloud frames, such asencoder 250, includes a point cloud re-sampling module 252, a 3-D motioncompensation and delta vector prediction module 254, a spatial imagegeneration module 256, a texture image generation module 258, and anattribute image generation module 260. In some embodiments, an encoderfor inter point cloud frames, such as encoder 250, may also include animage padding module 262 and a video compression module 264. An encoderfor inter point cloud frames, such as encoder 250, may generatecompressed point cloud information, such as compressed point cloudinformation 266. In some embodiments, the compressed point cloudinformation may reference point cloud information previously encoded bythe encoder, such as information from or derived from one or morereference image frames. In this way an encoder for inter point cloudframes, such as encoder 250, may generate more compact compressed pointcloud information by not repeating information included in a referenceimage frame, and instead communicating differences between the referenceframes and a current state of the point cloud.

In some embodiments, an encoder, such as encoder 250, may be combinedwith or share modules with an intra point cloud frame encoder, such asencoder 200. In some embodiments, a point cloud re-sampling module, suchas point cloud re-sampling module 252, may resample points in an inputpoint cloud image frame in order to determine a one-to-one mappingbetween points in patches of the current image frame and points inpatches of a reference image frame for the point cloud. In someembodiments, a 3D motion compensation & delta vector prediction module,such as a 3D motion compensation & delta vector prediction module 254,may apply a temporal prediction to the geometry/texture/attributes ofthe resampled points of the patches. The prediction residuals may bestored into images, which may be padded and compressed by usingvideo/image codecs. In regard to spatial changes for points of thepatches between the reference frame and a current frame, a 3D motioncompensation & delta vector prediction module 254, may determinerespective vectors for each of the points indicating how the pointsmoved from the reference frame to the current frame. A 3D motioncompensation & delta vector prediction module 254, may then encode themotion vectors using different image parameters. For example, changes inthe X direction for a point may be represented by an amount of redincluded at the point in a patch image that includes the point. In asimilar manner, changes in the Y direction for a point may berepresented by an amount of blue included at the point in a patch imagethat includes the point. Also, in a similar manner, changes in the Zdirection for a point may be represented by an amount of green includedat the point in a patch image that includes the point. In someembodiments, other characteristics of an image included in a patch imagemay be adjusted to indicate motion of points included in the patchbetween a reference frame for the patch and a current frame for thepatch.

Example Inter-Frame Decoder

FIG. 2D illustrates components of a decoder for decoding inter pointcloud frames, according to some embodiments. In some embodiments, adecoder, such as decoder 280, includes a video decompression module 270,an inverse 3D motion compensation and inverse delta prediction module272, a point cloud generation module 274, and a smoothing filter 276. Insome embodiments, a decoder, such as decoder 280 may be combined with adecoder, such as decoder 230, or may share some components with thedecoder, such as a video decompression module and/or smoothing filter.In decoder 280, the video/image streams are first decoded, then aninverse motion compensation and delta prediction procedure may beapplied. The obtained images are then used in order to reconstruct apoint cloud, which may be smoothed as described previously to generate areconstructed point cloud 282.

Segmentation Process

FIG. 3A illustrates an example segmentation process for determiningpatches for a point cloud, according to some embodiments. Thesegmentation process as described in FIG. 3A may be performed by adecomposition into patches module, such as decomposition into patchesmodule 206. A segmentation process may decompose a point cloud into aminimum number of patches (e.g., a contiguous subset of the surfacedescribed by the point cloud), while making sure that the respectivepatches may be represented by a depth field with respect to a patchplane. This may be done without a significant loss of shape information.

In some embodiments, a segmentation process comprises:

-   -   Letting point cloud PC be the input point cloud to be        partitioned into patches and {P(0), P(1) . . . , P(N−1)} be the        positions of points of point cloud PC.    -   In some embodiments, a fixed set D={D(0), D(1), . . . , D(K−1)}        of K 3D orientations is pre-defined. For instance, D may be        chosen as follows D={(1.0, 0.0, 0.0), (0.0, 1.0, 0.0), (0.0,        0.0, 1.0), (−1.0, 0.0, 0.0), (0.0, −1.0, 0.0), (0.0, 0.0, −1.0)}    -   In some embodiments, the normal vector to the surface at every        point P(i) is estimated. Any suitable algorithm may be used to        determine the normal vector to the surface. For instance, a        technique could include fetching the set H of the “N” nearest        points of P(i), and fitting a plane Π(i) to H(i) by using        principal component analysis techniques. The normal to P(i) may        be estimated by taking the normal ∇(i) to Π(i). Note that “N”        may be a user-defined parameter or may be found by applying an        optimization procedure. “N” may also be fixed or adaptive. The        normal values may then be oriented consistently by using a        minimum-spanning tree approach.    -   Normal-based Segmentation: An initial segmentation S0 of the        points of point cloud PC may be obtained by associating        respective points with the direction D(k) which maximizes the        score        ∇(i)|D(k)        , where        .|.        is the canonical dot product of R3. Pseudo code is provided        below.

for (i = 0; i < pointCount; ++i) {  clusterIndex = 0;  bestScore =

∇(i)|D(0) 

;  for(j = 1; j < K; ++j) {   score =

∇(i)|D(j) 

;   if (score > bestScore) {    bestScore = score;    clusterIndex = j;  }  }  partition[i]= clusterIndex; }

-   -   Iterative segmentation refinement: Note that segmentation S0        associates respective points with the plane Π(i) that best        preserves the geometry of its neighborhood (e.g. the        neighborhood of the segment). In some circumstances,        segmentation S0 may generate too many small connected components        with irregular boundaries, which may result in poor compression        performance. In order to avoid such issues, the following        iterative segmentation refinement procedure may be applied:        -   1. An adjacency graph A may be built by associating a vertex            V(i) to respective points P(i) of point cloud PC and by            adding R edges {E(i,j(0)), . . . , EN(R−1)} connecting            vertex V(i) to its nearest neighbors {V(j(0)), V(j(1)), . .            . , V(j(R−1))}. More precisely, {V(j(0)), V(j(1)), . . . ,            V(j(R−1))} may be the vertices associated with the points            {P(j(0)), P(j(1)), . . . , P(j(R−1))}, which may be the            nearest neighbors of P(i). Note that R may be a user-defined            parameter or may be found by applying an optimization            procedure. It may also be fixed or adaptive.        -   2. At each iteration, the points of point cloud PC may be            traversed and every vertex may be associated with the            direction D (k) that maximizes

$\left( {\left\langle {{{\nabla(i)}\left. {D(k)} \right\rangle} + {\frac{\lambda}{R}{{\zeta(i)}}}} \right),} \right.$

-   -   -    where |ζ(i)| is the number of the R-nearest neighbors of            V(i) belonging to the same cluster and λ is a parameter            controlling the regularity of the produced patches. Note            that the parameters λ and R may be defined by the user or            may be determined by applying an optimization procedure.            They may also be fixed or adaptive. In some embodiments, a            “user” as referred to herein may be an engineer who            configured a point cloud compression technique as described            herein to one or more applications.        -   3. An example of pseudo code is provided below

for(I = 0; I < iterationCount; ++I) {   for(i = 0; i < pointCount; ++i){    clusterIndex = partition[i];    bestScore = 0.0;    for(k = 0; k <K; ++k) {     score =

∇(i)|D(k)

;     for(j ϵ {j(0), j(1), ..., j(R − 1)}) {      if (k == partition[j]){       score ${+=\frac{\lambda}{R}};$      }     }     if (score >bestScore) {      bestScore = score;      clusterIndex = k;     }    }   partition[i] = clusterIndex;   }  }

-   -   -    *In some embodiments, the pseudo code shown above may            further include an early termination step. For example, if a            score that is a particular value is reached, or if a            difference between a score that is reached and a best score            only changes by a certain amount or less, the search could            be terminated early. Also, the search could be terminated if            after a certain number of iterations (l=m), the clusterindex            does not change.

    -   Patch segmentation: In some embodiments, the patch segmentation        procedure further segments the clusters detected in the previous        steps into patches, which may be represented with a depth field        with respect to a projection plane. The approach proceeds as        follows, according to some embodiments:        -   1. First, a cluster-based adjacency graph with a number of            neighbors R′ is built, while considering as neighbors only            the points that belong to the same cluster. Note that R′ may            be different from the number of neighbors R used in the            previous steps.        -   2. Next, the different connected components of the            cluster-based adjacency graph are extracted. Only connected            components with a number of points higher than a parameter α            are considered. Let CC={CC(0), CC(1), . . . , CC(M−1)} be            the set of the extracted connected components.        -   3. Respective connected component CC(m) inherits the            orientation D(m) of the cluster it belongs to. The points of            CC(m) are then projected on a projection plane having as            normal the orientation D(m), while updating a depth map,            which records for every pixel the depth of the nearest point            to the projection plane.        -   4. An approximated version of CC(m), denoted C′(m), is then            built by associating respective updated pixels of the depth            map with a 3D point having the same depth. Let PC′ be the            point cloud obtained by the union of reconstructed connected            components {CC′(0), CC′(1), . . . , CC′(M−1)}        -   5. Note that the projection reconstruction process may be            lossy and some points may be missing. In order, to detect            such points, every point P(i) of point cloud PC may be            checked to make sure it is within a distance lower than a            parameter δ from a point of PC′. If this is not the case,            then P(i) may be marked as a missed point and added to a set            of missed points denoted MP.        -   6. The steps 2-5 are then applied to the missed points MP.            The process is repeated until MP is empty or CC is empty.            Note that the parameters δ and α may be defined by the user            or may be determined by applying an optimization procedure.            They may also be fixed or adaptive.        -   7. A filtering procedure may be applied to the detected            patches in order to make them better suited for compression.            Example filter procedures may include:            -   a. A smoothing filter based on the                geometry/texture/attributes of the points of the patches                (e.g., median filtering), which takes into account both                spatial and temporal aspects.            -   b. Discarding small and isolated patches.            -   c. User-guided filtering.            -   d. Other suitable smoothing filter techniques.                Layers

The image generation process described above consists of projecting thepoints belonging to each patch onto its associated projection plane togenerate a patch image. This process could be generalized to handle thesituation where multiple points are projected onto the same pixel asfollows:

-   -   Let H(u, v) be the set of points of the current patch that get        projected to the same pixel (u,v). Note that H(u, v) may be        empty, may have one point or multiple points.    -   If H(u, v) is empty then the pixel is marked as unoccupied.    -   If the H(u, v) has a single element, then the pixel is filled        with the associated geometry/texture/attribute value.    -   If H(u,v), has multiple elements, then different strategies are        possible:        -   Keep only the nearest point P0(u,v) for the pixel (u,v)        -   Take the average or a linear combination of a group of            points that are within a distance d from P0(u,v), where d is            a user-defined parameter needed only on the encoder side.        -   Store two images: one for P0(u,v) and one to store the            furthest point P1(u, v) of H(u, v) that is within a distance            d from P0(u,v)        -   Store N patch images containing a subset of H(u, v)

The generated patch images for point clouds with points at the samepatch location, but different depths may be referred to as layersherein. In some embodiments, scaling/up-sampling/down-sampling could beapplied to the produced patch images/layers in order to control thenumber of points in the reconstructed point cloud.

Guided up-sampling strategies may be performed on the layers that weredown-sampled given the full resolution image from another “primary”layer that was not down-sampled.

In some embodiments, a delta prediction between layers could beadaptively applied based on a rate-distortion optimization. This choicemay be explicitly signaled in the bit stream.

In some embodiments, the generated layers may be encoded with differentprecisions. The precision of each layer may be adaptively controlled byusing a shift+scale or a more general linear or non-lineartransformation.

In some embodiments, an encoder may make decisions on a scaling strategyand parameters, which are explicitly encoded in the bit stream. Thedecoder may read the information from the bit stream and apply the rightscaling process with the parameters signaled by the encoder.

In some embodiments, a video encoding motion estimation process may beguided by providing a motion vector map to the video encoder indicatingfor each block of the image frame, a 2D search center or motion vectorcandidates for the refinement search. Such information, may be trivialto compute since the mapping between the 3D frames and the 2D imageframes is available to the point cloud encoder and a coarse mappingbetween the 2D image frames could be computed by using a nearestneighbor search in 3D.

The video motion estimation/mode decision/intra-prediction could beaccelerated/improved by providing a search center map, which may provideguidance on where to search and which modes to choose from for each N×Npixel block.

Hidden/non-displayed pictures could be used in codecs such as AV1 andHEVC. In particular, synthesized patches could be created and encoded(but not displayed) in order to improve prediction efficiency. Thiscould be achieved by re-using a subset of the padded pixels to storesynthesized patches.

The patch re-sampling (e.g., packing and patch segmentation) processdescribed above exploits solely the geometry information. A morecomprehensive approach may take into account the distortions in terms ofgeometry, texture, and other attributes and may improve the quality ofthe re-sampled point clouds.

Instead of first deriving the geometry image and optimizing the textureimage given said geometry, a joint optimization of geometry and texturecould be performed. For example, the geometry patches could be selectedin a manner that results in minimum distortion for both geometry andtexture. This could be done by immediately associating each possiblegeometry patch with its corresponding texture patch and computing theircorresponding distortion information. Rate-distortion optimization couldalso be considered if the target compression ratio is known.

In some embodiments, a point cloud resampling process described abovemay additionally consider texture and attributes information, instead ofrelying only on geometry.

Also, a projection-based transformation that maps 3D points to 2D pixelscould be generalized to support arbitrary 3D to 2D mapping as follows:

-   -   Store the 3D to 2D transform parameters or the pixel coordinates        associated with each point    -   Store X, Y, Z coordinates in the geometry images instead of or        in addition to the depth information        Packing

In some embodiments, depth maps associated with patches, also referredto herein as “depth patch images,” such as those described above, may bepacked into a 2D image frame. For example, a packing module, such aspacking module 208, may pack depth patch images generated by a spatialimage generation module, such as spatial image generation module 210.The depth maps, or depth patch images, may be packed such that (A) nonon-overlapping block of T×T pixels contains depth information from twodifferent patches and such that (B) a size of the generated image frameis minimized.

In some embodiments, packing comprises the following steps:

-   -   a. The patches are sorted by height and then by width. The        patches are then inserted in image frame (I) one after the other        in that order. At each step, the pixels of image frame (I) are        traversed in raster order, while checking if the current patch        could be inserted under the two conditions (A) and (B) described        above. If it is not possible then the height of (I) is doubled.    -   b. This process is iterated until all the patches are inserted.

In some embodiments, the packing process described above may be appliedto pack a subset of the patches inside multiples tiles of an image frameor multiple image frames. This may allow patches with similar/closeorientations based on visibility according to the rendering cameraposition to be stored in the same image frame/tile, to enableview-dependent streaming and/or decoding. This may also allow parallelencoding/decoding.

In some embodiments, the packing process can be considered a bin-packingproblem and a first decreasing strategy as described above may beapplied to solve the bin-packing problem. In other embodiments, othermethods such as the modified first fit decreasing (MFFD) strategy may beapplied in the packing process.

In some embodiments, if temporal prediction is used, such as describedfor inter compression encoder 250, such an optimization may be performedwith temporal prediction/encoding in addition to spatialprediction/encoding. Such consideration may be made for the entire videosequence or per group of pictures (GOP). In the latter case additionalconstraints may be specified. For example, a constraint may be that theresolution of the image frames should not exceed a threshold amount. Insome embodiments, additional temporal constraints may be imposed, evenif temporal prediction is not used, for example such as that a patchcorresponding to a particular object view is not moved more than xnumber of pixels from previous instantiations.

FIG. 3B illustrates an example image frame comprising packed patchimages and padded portions, according to some embodiments. Image frame300 includes patch images 302 packed into image frame 300 and alsoincludes padding 304 in space of image frame 300 not occupied by patchimages. In some embodiments, padding, such as padding 304, may bedetermined so as to minimize incongruences between a patch image and thepadding. For example, in some embodiments, padding may construct newpixel blocks that are replicas of, or are to some degree similar to,pixel blocks that are on the edges of patch images. Because an imageand/or video encoder may encode based on differences between adjacentpixels, such an approach may reduce the number of bytes required toencode an image frame comprising of patch images and padding, in someembodiments.

In some embodiments, the patch information may be stored in the sameorder as the order used during the packing, which makes it possible tohandle overlapping 2D bounding boxes of patches. Thus a decoderreceiving the patch information can extract patch images from the imageframe in the same order in which the patch images were packed into theimage frame. Also, because the order is known by the decoder, thedecoder can resolve patch image bounding boxes that overlap.

FIG. 3C illustrates an example image frame 312 with overlapping patches,according to some embodiments. FIG. 3C shows an example with two patches(patch image 1 and patch image 2) having overlapping 2D bounding boxes314 and 316 that overlap at area 318. In order to determine to whichpatch the T×T blocks in the area 318 belong, the order of the patchesmay be considered. For example, the T×T block 314 may belong to the lastdecoded patch. This may be because in the case of an overlapping patch,a later placed patch is placed such that it overlaps with a previouslyplaced patch. By knowing the placement order it can be resolved thatareas of overlapping bounding boxes go with the latest placed patch. Insome embodiments, the patch information is predicted and encoded (e.g.,with an entropy/arithmetic encoder). Also, in some embodiments, U0, V0,DU0 and DV0 are encoded as multiples of T, where T is the block sizeused during the padding phase.

FIG. 3C also illustrates blocks of an image frame 312, wherein theblocks may be further divided into sub-blocks. For example block A1, B1,C1, A2, etc. may be divided into multiple sub-blocks, and, in someembodiments, the sub-blocks may be further divided into smaller blocks.In some embodiments, a video compression module of an encoder, such asvideo compression module 218 or video compression module 264, maydetermine whether a block comprises active pixels, non-active pixels, ora mix of active and non-active pixels. The video compression module maybudget fewer resources to compress blocks comprising non-active pixelsthan an amount of resources that are budgeted for encoding blockscomprising active pixels. In some embodiments, active pixels may bepixels that include data for a patch image and non-active pixels may bepixels that include padding. In some embodiments, a video compressionmodule may sub-divide blocks comprising both active and non-activepixels, and budget resources based on whether sub-blocks of the blockscomprise active or non-active pixels. For example, blocks A1, B1, C1, A2may comprise non-active pixels. As another example block E3 may compriseactive pixels, and block B6, as an example, may include a mix of activeand non-active pixels.

In some embodiments, a patch image may be determined based onprojections, such as projecting a point cloud onto a cube, cylinder,sphere, etc. In some embodiments, a patch image may comprise aprojection that occupies a full image frame without padding. Forexample, in a cubic projection each of the six cubic faces may be apatch image that occupies a full image frame.

For example, FIG. 3D illustrates a point cloud being projected ontomultiple projections, according to some embodiments.

In some embodiments, a representation of a point cloud is encoded usingmultiple projections. For example, instead of determining patches for asegment of the point cloud projected on a plane perpendicular to anormal to the segment, the point cloud may be projected onto multiplearbitrary planes or surfaces. For example, a point cloud may beprojected onto the sides of a cube, cylinder, sphere, etc. Also multipleprojections intersecting a point cloud may be used. In some embodiments,the projections may be encoded using conventional video compressionmethods, such as via a video compression module 218 or video compressionmodule 264. In particular, the point cloud representation may be firstprojected onto a shape, such as a cube, and the differentprojections/faces projected onto that shape (i.e. front (320), back(322), top (324), bottom (326), left (328), right (330)) may all bepacked onto a single image frame or multiple image frames. Thisinformation, as well as depth information may be encoded separately orwith coding tools such as the ones provided in the 3D extension of theHEVC (3D-HEVC) standard. The information may provide a representation ofthe point cloud since the projection images can provide the (x,y)geometry coordinates of all projected points of the point cloud.Additionally, depth information that provides the z coordinates may beencoded. In some embodiments, the depth information may be determined bycomparing different ones of the projections, slicing through the pointcloud at different depths. When projecting a point cloud onto a cube,the projections might not cover all point cloud points, e.g. due toocclusions. Therefore additional information may be encoded to providefor these missing points and updates may be provided for the missingpoints.

In some embodiments, adjustments to a cubic projection can be performedthat further improve upon such projections. For example, adjustments maybe applied at the encoder only (non-normative) or applied to both theencoder and the decoder (normative).

More specifically, in some embodiments alternative projections may beused. For example, instead of using a cubic projection, a cylindrical orspherical type of a projection method may be used. Such methods mayreduce, if not eliminate, redundancies that may exist in the cubicprojection and reduce the number or the effect of “seams” that may existin cubic projections. Such seams may create artifacts at objectboundaries, for example. Eliminating or reducing the number or effect ofsuch seams may result in improved compression/subjective quality ascompared to cubic projection methods. For a spherical projection case, avariety of sub-projections may be used, such as the equirectangular,equiangular, and authagraph projection among others. These projectionsmay permit the projection of a sphere onto a 2D plane. In someembodiments, the effects of seams may be de-emphasized by overlappingprojections, wherein multiple projections are made of a point cloud, andthe projections overlap with one another at the edges, such that thereis overlapping information at the seams. A blending effect could beemployed at the overlapping seams to reduce the effects of the seams,thus making them less visible.

In addition to, or instead of, considering a different projection method(such as cylindrical or spherical projections), in some embodimentsmultiple parallel projections may be used. The multiple parallelprojections may provide additional information and may reduce a numberof occluded points. The projections may be known at the decoder orsignaled to the decoder. Such projections may be defined on planes orsurfaces that are at different distances from a point cloud object.Also, in some embodiments the projections may be of different shapes,and may also overlap or cross through the point cloud object itself.These projections may permit capturing some characteristics of a pointcloud object that may have been occluded through a single projectionmethod or a patch segmentation method as described above.

For example, FIG. 3E illustrates a point cloud being projected ontomultiple parallel projections, according to some embodiments. Pointcloud 350 which includes points representing a coffee mug is projectedonto parallel horizontal projections 352 that comprise planes orthogonalto the Z axis. Point cloud 350 is also projected onto verticalprojections 354 that comprise planes orthogonal to the X axis, and isprojected onto vertical projections 356 that comprise planes orthogonalto the Y axis. In some embodiments, instead of planes, multipleprojections may comprise projections having other shapes, such asmultiple cylinders or spheres.

Generating Images Having Depth

In some embodiments, only a subset of the pixels of an image frame willbe occupied and may correspond to a subset of 3D points of a pointcloud. Mapping of patch images may be used to generate geometry,texture, and attribute images, by storing for each occupied pixel thedepth/texture/attribute value of its associated point.

In some embodiments, spatial information may be stored with variousvariations, for example spatial information may:

-   -   a. Store depth as a monochrome image.    -   b. Store depth as Y and keep U and V empty (where YUV is a color        space, also RGB color space may be used).    -   c. Store depth information for different patches in different        color planes Y, U and V, in order to avoid inter-patch        contamination during compression and/or improve compression        efficiency (e.g., have correlated patches in the same color        plane). Also, hardware codec capabilities may be utilized, which        may spend the same encoding\decoding time independently of the        content of the frame.    -   d. Store depth patch images on multiple images or tiles that        could be encoded and decoded in parallel. One advantage is to        store depth patch images with similar/close orientations or        based on visibility according to the rendering camera position        in the same image/tile, to enable view-dependent streaming        and/or decoding.    -   e. Store depth as Y and store a redundant version of depth in U        and V.    -   f Store X, Y, Z coordinates in Y, U, and V    -   g. Different bit depth (e.g., 8, 10 or 12-bit) and sampling        (e.g., 420, 422, 444 . . . ) may be used. Note that different        bit depth may be used for the different color planes.        Padding

In some embodiments, padding may be performed to fill the non-occupiedpixels with values such that the resulting image is suited forvideo/image compression. For example, image frame padding module 216 orimage padding module 262 may perform padding as described below.

In some embodiments, padding is applied on pixels blocks, while favoringthe intra-prediction modes used by existing video codecs. Moreprecisely, for each block of size B×B to be padded, the intra predictionmodes available at the video encoder side are assessed and the one thatproduces the lowest prediction errors on the occupied pixels isretained. This may take advantage of the fact that video/image codecscommonly operate on pixel blocks with pre-defined sizes (e.g., 64×64,32×32, 16×16 . . . ). In some embodiments, other padding techniques mayinclude linear extrapolation, in-painting techniques, or other suitabletechniques.

Video Compression

In some embodiments, a video compression module, such as videocompression module 218 or video compression module 264, may performvideo compression as described below.

In some embodiments, a video encoder may leverage an occupancy map,which describes for each pixel of an image whether it stores informationbelonging to the point cloud or padded pixels. In some embodiments, suchinformation may permit enabling various features adaptively, such asde-blocking, adaptive loop filtering (ALF), or shape adaptive offset(SAO) filtering. Also, such information may allow a rate control moduleto adapt and assign different, e.g. lower, quantization parameters(QPs), and in an essence a different amount of bits, to the blockscontaining the occupancy map edges. Coding parameters, such aslagrangian multipliers, quantization thresholding, quantizationmatrices, etc. may also be adjusted according to the characteristics ofthe point cloud projected blocks. In some embodiments, such informationmay also enable rate distortion optimization (RDO) and ratecontrol/allocation to leverage the occupancy map to consider distortionsbased on non-padded pixels. In a more general form, weighting ofdistortion may be based on the “importance” of each pixel to the pointcloud geometry. Importance may be based on a variety of aspects, e.g. onproximity to other point cloud samples,directionality/orientation/position of the samples, etc. Facing forwardsamples, for example, may receive a higher weighting in the distortioncomputation than backward facing samples. Distortion may be computedusing metrics such as Mean Square or Absolute Error, but differentdistortion metrics may also be considered, such as SSIM, VQM, VDP,Hausdorff distance, and others.

Occupancy Map Compression

In some embodiments, an occupancy map compression module, such asoccupancy map compression module 220, may compress an occupancy map asdescribed below.

Example Occupancy Map Compression Techniques

In some embodiments, an occupancy map may be encoded in a hierarchicalmode. Such a process may comprise:

-   -   1. A binary information for each B1×B2 pixel block (e.g., a        rectangle that covers the entire image frame, or smaller blocks        of different sizes such as 64×64, 64×32, 32×32 block, etc.)        being encoded indicating whether the block is empty (e.g., has        only padded pixels) or non-empty (e.g., has non-padded pixels).    -   2. If the block is non-empty, then a second binary information        may be encoded to indicate whether the block is full (e.g., all        the pixels are non-padded) or not.    -   3. The non-empty and non-full blocks may then be refined by        considering their (B1/2)×(B2/2) sub-blocks.    -   4. The steps 1-3 may be repeated until the size of the block        reaches a certain block size B3×B4 (e.g., of size 4×4). At this        level only the empty/non-empty information may be encoded.    -   5. An entropy-based codec may be used to encode the binary        information in steps 1 and 2. For instance, context adaptive        binary arithmetic encoders may be used.    -   6. The reconstructed geometry image may be leveraged to better        encode the occupancy map. More precisely, the residual        prediction errors may be used to predict whether a block is        empty or not or full or not. Such an information may be        incorporated by using a different context based on the predicted        case or simply by encoding the binary value XORed with the        predicted value.

In some embodiments, mesh-based codecs may be an alternative to theapproach described above.

Additional Example Occupancy Map Compression Technique

In some embodiments, auxiliary information and the patch encoding ordermay be leveraged in order to efficiently compress a mapping informationindicating for each T×T block of an image frame (e.g., 16×16 block) towhich patch it belongs to. This mapping may be explicitly encoded in thebit stream as follows:

-   -   A list of candidate patches is created for each T×T block of an        image frame by considering all the patches that overlap with        that block.    -   The list of candidates is sorted in the reverse order of the        patches. Meaning the candidate patches are ordered from smallest        patch to largest patch.    -   For each block, the index of the patches in this list is encoded        by using an arithmetic or other form of an entropy encoder (e.g.        UVLC or Huffman based).    -   Note that empty blocks are assigned a special index, such as        zero.    -   The mapping information described above makes it possible to        detect empty T×T blocks of an image frame (e.g., blocks that        contain only padded pixels). The occupancy information is        encoded only for the non-empty T×T blocks (e.g., the blocks that        contain at least one non-padded pixel).    -   The occupancy map is encoded with a precision of a B0×B0 blocks.        In order to achieve lossless encoding B0 is chosen to be 1. In        some embodiments B0=2 or B0=4, which may result in visually        acceptable results, while significantly reducing the number of        bits required to encode the occupancy map.    -   Binary values are associated with B0×B0 sub-blocks belonging to        the same T×T block. Different strategies are possible. For        instance, one could associate a value of 1 if the sub-block        contains at least some non-padded pixels and 0 otherwise. If a        sub-block has a value of 1 it is said to be full, otherwise it        is an empty sub-block.    -   If all the sub-blocks of a T×T block are full (e.g., have value        1). The block is said to be full. Otherwise, the block is said        to be non-full.    -   A binary information is encoded for each T×T block to indicate        whether it is full or not. Various encoding strategies could be        used. For instance, a context adaptive binary arithmetic encoder        could be used.    -   If the block is non-full, an extra information is encoded        indicating the location of the full/empty sub-blocks. More        precisely, the process may proceed as follows:        -   Different traversal orders are defined for the sub-blocks.            FIG. 12B, shows some examples. The traversal orders are            predetermined and known to both the encoder and decoder.        -   The encoder chooses one of the traversal orders and            explicitly signals its index in the bit stream.        -   The binary values associated with the sub-blocks are encoded            by using a run-length encoding strategy.        -   The binary value of the initial sub-block is encoded.            Various encoding strategies could be used. For instance,            fixed length coding or a context adaptive binary arithmetic            encoder could be used.        -   Continuous runs of Os and is are detected, while following            the traversal order selected by the encoder.        -   The number of detected runs is encoded. Various encoding            strategies could be used. For instance, fixed length coding            or a context adaptive binary arithmetic encoder, or a            universal variable length encoder (UVLC) could be used.        -   The length of each run, except of the last one, is then            encoded. Various encoding strategies could be used. For            instance, fixed length coding, a context adaptive binary            arithmetic encoder, or a universal variable length encoder            could be used.

Note that the symbol probabilities used during the arithmetic encodingcould be initialized by using values explicitly signaled in the bitstream by the encoder in order to improve compression efficiency. Suchinformation could be signaled at frame, slice, row(s) of blocks, orblock level, or using a non-fixed interval. In that case, a system mayhave the ability to signal the initialization interval, or the intervaladaptation could be predefined between encoder and decoder. For example,the interval could start with one block, and then increment by one blockafterwards (e.g. using an adaptation positions of {1, 2, 3 . . . N−1 . .. } blocks.

The choice of the traversal order may have a direct impact on thecompression efficiency. Different strategies are possible. For instance,the encoder could choose the traversal order, which would result in thelowest number of bits or the lowest number of runs. In some embodiments,hierarchical sub-blocks with variable sizes may be used.

In some embodiments, temporal prediction may be used forencoding/compressing occupancy maps as follows:

-   -   a. The occupancy map of the current frame may be predicted from        the occupancy map of a reference frame (e.g. through a        difference process assuming zero motion). The prediction could        be done at the frame level, but could also be done at a        sub-block level, e.g. signal 1 bit whether a block will be        predicted temporally, or the original map for a block will be        used instead.    -   b. Prediction could be enhanced by using motion compensation and        by associating a motion vector with each T×T block.    -   c. The values of the current block may be XOR-ed with the values        of the block referenced by the motion vector or the co-located        block. If no prediction is used, the current block may be coded        as is.    -   d. Motion vectors could be integer, integer multiples, or can        have sub-pixel precision.    -   e. The encoding strategy described above may be applied to the        results.    -   f. The motion vectors of the current block may be predicted        based on the motion vectors of the previously encoded blocks.        For example, a list of candidate predicted motion vectors may be        computed based on the motion vectors of spatially and/or        temporally neighboring blocks that have already been encoded.        The index of the best candidate to be used as a predictor and        the difference can be explicitly encoded in the bit stream. The        process may be similar to the process used in codecs such as AVC        and HEVC among others. A reduction in temporal candidates may be        performed similar to what is done in HEVC to reduce memory        requirements. The residual motion vector can then be encoded        using a technique such as context adaptive arithmetic encoding        or UVLC.    -   g. A skip mode may also be supported to indicate that the        predicted block matches exactly the reference block. In that        case, no residual motion vector is needed.    -   h. Different block sizes could be used instead of sticking with        T×T blocks.    -   i. The choice of the block size and the motion vectors could be        achieved by minimizing the number of bits required to encode the        occupancy map.    -   j. The process could also consider multiple references.

In some embodiments, additional techniques for encoding/compression ofan occupancy map may include:

-   -   Using clues included in the video picture to help encode the        occupancy map, such as:        -   Use high quantization parameters QPs (e.g., 51) or use skip            mode for blocks composed of padded pixels only.        -   The arithmetic encoding contexts could be adaptively            adjusted based on information extracted from the video bit            streams associated with the texture/geometry/motion frames.    -   Group the binary values associated with pixels into 8-bit or        10-bit words and encode them with dictionary-based approaches        such as the DEFLATE algorithm.        -   Pixels could be grouped 4×2/5×2 blocks or by leveraging a            zig zag scan.        -   Only the pixels belonging to non-empty T×T blocks may be            encoded.        -   The mapping information indicating for each T×T block to            which patch it belongs may encoded.            Additional Example Occupancy Map Compression Techniques

In some embodiments, a binary occupancy map is generated based onwhether or not bocks of the occupancy map are occupied or un-occupied.This may be performed in a similar manner as described above. Also, thepatch information (e.g. bounding box position, size, etc.) is encodedusing an arithmetic encoder, in a similar manner as described above.However, instead of relying on the occupancy map to discard empty blocksthat intersect with at least one patch bounding box, the empty boxes areexplicitly signaled with a special value for the local index. In thisapproach, the block to patch information is decoded when needed.

In some embodiments, instead of using an arithmetic encoder as describedabove to encode block to patch information that links boxes of theoccupancy map with particular patches, the block to patch information(which contains the local indexes) may be encoded using a video-basedencoder. The encoded block-to patch information may then be decodedusing a corresponding video-decoder.

In some embodiments, instead of generating a binary occupancy map basedon whether or not bocks of the occupancy map are occupied orun-occupied, a non-binary occupancy map is generated. The non-binaryoccupancy map is configured such that each pixel not only indicateswhether the pixel is occupied or non-occupied, but also includes anattribute value, such as a color value that is associated with a localindex value of a patch with which the pixel is associated. If the pixelis non-occupied, the pixel may have a color value of zero. Also, thepatch information (e.g. bounding box position, size, etc.) is encodedusing an arithmetic encoder, in a similar manner as described above. Thenon-binary occupancy map may be encoded using a video-based encoder. Adecoder can retrieve the block to patch information by decoding thenon-binary occupancy map and matching each pixel value with the localindex lists.

In some embodiments, instead of using a local index, a full list ofpatches may be used as an index. In such embodiments, there may be noneed to compute a list of candidate patches for each block. The decodercan retrieve the block-to-patch information by decoding the non-binaryoccupancy map directly reading the index value for the patch associatedwith the pixel from the value of the pixel. In such embodiments, thelocal index may be omitted because there are enough unique values (e.g.non-binary) values available to be associated with a block, such thateach candidate patch may be assigned a unique value.

In some embodiments, during the generation of the occupancy map, thebounding boxes for the patches may be adjusted or initially packed in animage frame such that the bounding boxes do not overlap. This removesambiguity as to whether a particular bounding box belongs to aparticular patch or another patch. The patch information (withnon-overlapping bounding boxes) is encoding using an arithmetic encoder.Because there is not ambiguity as to which patch goes with whichbounding box, the block to patch information (such as in the local indexor complete index, as described above), may be omitted.

In some embodiments, a process that uses a full list of patches (insteadof a local index) may result in a high number of patches, which mayexceed the max possible number of values (e.g. color values) that may berepresented in the non-binary occupancy map. In some embodiments, toaddress such issues, an occupancy map may be decomposed into segments,with a limited number of patches per segments. Thus for each segment,the patch index is bound. For example, fewer patches may be listed aspossibilities for a segment of an occupancy map, such that for eachsegment the list of possible patches is less than the max possiblenumber of values (e.g. color values). In some such embodiments, boundingboxes for different patches may be allowed to overlap within a segment,but not across segments. During decoding, each segment may have its ownglobal index list of possible patches for that segment.

In some embodiments, a binary occupancy map is generated such that whenthe patches are packed in the image frame, a bounding box of the patch,aligned to an occupancy resolution does not intersect any previouslypacked patches of size=occupancy resolution*size occupancy resolution(e.g. a 16×16 block). The patch information (e.g. bounding box positionand size) for each patch is encoded using an arithmetic encoder. Theorder in which the patch information for each patch is encoded maycreate a hierarchy of patches, such that for any overlapping boundingboxes, the corresponding patch that goes with the bound box can beresolved based on the hierarchy of patch information. The decoder mayreconstruct block to patch information using the arithmetically encodedpatch information (without the block to patch information beingexplicitly encoded). For example, a patch list may be parsed in a sameorder at a decoder as an order in which the patch list was generated atencoding time, wherein the order indicates an order in which the patcheswere packed in the image frame. This is possible because the packingguarantees that the bounding box for a given patch does not cover anypreviously processed patch. In such embodiments, patches may be packed(and signaled) in an order such as from small to large, or vice versa.During the packing, each block may include pixels of just one patch, butsome bounding boxes for multiple patches may overlap, wherein blocks ofthe overlapping patches include no pixels for either patch, or pixelsfor just one of the patches, but not pixels for more than one patch.

Auxiliary Patch-Information Compression

In some embodiments, for each patch, the following information may beencoded. For example, by auxiliary patch-info compression module 222.

-   -   Its location (U0, V0) in the packed image frame and the extent        of its 2D bounding box (DU0, DV0).    -   Minimum/maximum/average/median depth value.    -   Index of the projection direction.        Video-Based Occupancy Map Compression

As described above, in some embodiments, the occupancy map is a binaryinformation that indicates for each pixel in the image frame whether thepixel should be interpreted as an actual point in the point cloud ornot, e.g. the pixel is a padded pixel or not. Also, as described above,the auxiliary patch-information indicates for each T×T block of an imageframe to which patch it belongs. Whereas it was described above toencode an index of patches for a block of an image frame and to keepencoding information for sub-blocks of the image frame until thesub-blocks were either fully empty or fully occupied, an alternativeapproach is to use a video encoder to encode an additional image framefor the occupancy map. In such embodiments, the additional occupancy mapimage frame, indicates occupied and unoccupied pixels based on imageproperties such as different colors (e.g. occupied pixels may be whiteand non-occupied pixels may be black). In this way it is not necessaryto completely subdivide the blocks of the image frame until onlyoccupied or un-occupied sub-blocks are determined. Instead, it is onlynecessary to identify bounding box sizes and locations in the imageframe for the respective patches. The video encoded occupancy map willmirror the image frame and the different pixel values in the occupancymap video image frame will indicate which pixels in a given bounding boxof the patch video image frame are patch image pixels or are paddedpixels. Thus there is not a need to create a bit stream of sub-dividedblocks of the image frame and there is not a need to indicate for eachsub-block whether the sub-block is full or empty. Instead the videoencoded occupancy map can be used to determine which pixels included ina patch bounding box are padded pixels or patch image pixels. In someembodiments, an occupancy map may be first encoded and then used togenerate an index of patches that are associated with blocks of an imageframe. In some embodiments, a compression process follows the followingprocedure that leverages existing video codecs to compress an occupancymap.

The occupancy map could be encoded with a precision of B0×B1 blocks. Inorder to achieve lossless encoding B0 and B1 may be chosen to be equalto 1. In practice B0=B1=2 or B0=B1=4 may result in visually acceptableresults, while significantly reducing the number of bits required toencode the occupancy map.

In some embodiments, a single binary is associated with each B0×B1sub-block. Different strategies are possible. For instance, one couldassociate a value of 1 with the rule that the sub-block contains atleast one non-padded pixel and the value of 0 if not. In order to reducecomputational complexity, the binary values of multiple B0×B1 blockscould be grouped together in a single pixel value.

A binary video frame may be generated by storing the value of each B0×B1block in a pixel. The obtained video frame could be compressed by usinga lossless video codec. For example the HEVC video codec could beutilized and its main, screen context coding (scc) main or otherprofiles could be used.

In some embodiments, the occupancy map could be packed in a 4:4:4 or4:2:0 chroma format, where the chroma information could contain fixedvalues, e.g. the values 0 or 128 for an 8 bit codec. The occupancy mapcould also be coded using a codec supporting a monochromerepresentation. The occupancy map could be replicated in all colorcomponents and encoded using a 4:4:4 representation. Otherrearrangements of the occupancy map could be used so as to fit the datain a 4:4:4, 4:2:2, or 4:2:0 representation, while preserving thelossless nature of the signal and at the same time preserving thelossless characteristics of the occupancy map. For example, theoccupancy map could be segmented to even horizontal and odd horizontalposition sub-maps, and those sub-maps could be embedded into a 4:4:4signal, the odd position samples in the Y plane and the even positionsamples in the U plane, and then encoded. This could provide savings incomplexity since a reduced resolution (by half) image would be encoded.Other such arrangements could be used.

The occupancy map is used to detect non-empty T×T blocks and only forthose blocks a patch index is encoded by proceeding as follows:

-   -   1) A list of candidate patches is created for each T×T block by        considering all the patches that contain that block.    -   2) The list of candidates is sorted in the reverse order of the        patches. Meaning the index is sorted from smallest patch to        largest patch, e.g. the patches with bounding boxes covering the        smallest area are ordered ahead of patches with bounding boxes        covering larger areas of the patch image frame.    -   3) For each block, the index of the patch in this list is        encoded by using an entropy encoder, e.g. an arithmetic encoder        or other suitable encoder.        Patch Alignment and Size Determination in a 2D Bounding Box of        an Occupancy Map

In some embodiments, methods may be applied to remove redundant outputpoints created by the occupancy map quantization/downsampling/upsamplingprocess. By removing these points, the reconstruction process can resultin better reconstruction. Furthermore, fewer points may need to beprocessed during post-processing, e.g. when performing smoothing asdescribed below, thus reducing reconstruction complexity as well asduring attribute image generation during encoding. Additionally, qualityof the “removed” points in the geometry and attribute layers may be lessimportant, therefore the characteristics of such points may be exploitedduring compression, such as devoting fewer resources to redundant pointsthat will be removed.

In some embodiments, when a patch is created, the patch size information(e.g. sizeU0, sizeV0) is defined as multiples of the occupancy packingblock. In other words, when patch size is N×M and the occupancy packingblock resolution is 16, sizeU0 and sizeV0 will be (16*(N/16+1),16*(M/16+1)). For example, Table 1 shows an example algorithm fordetermining the width and width of a 2D bounding box for a patch.

TABLE 1 Width and Height of Patch Derivation If p is equal too, then: Patch2dSizeU[ frmIdx ] [ p ] = pdu_2d_delta_size_u[ frmIdx ] [ p ] *ops_occupancy_packing_block_size (8-8)  Patch2dSizeV[ frmIdx ] [ p ] =pdu_2d_delta_size_v[ frmIdx ] [ p ] * ops_occupancy_packing_block_size(8-9) Otherwise, if (p > 0), then:  Patch2dSizeU[ frmIdx ] [ p ] =Patch2dSizeU[ frmIdx ] [p − 1 ] + pdu_2d_delta_size_u[ frmIdx ] [ p ] *ops_occupancy_packing_block_size (8-10)  Patch2dSizeV[ frmIdx ] [ p ] =Patch2dSizeV[ frmIdx ] [p − 1 ] + pdu_2d_delta_size_v[ frmIdx ] [ p ] *ops_occupancy_packing_block_size (8-11)

In some embodiments, in a patch bounding box, there could be “empty”lines and/or columns maximum equal to (occupancy packing blockresolution−1).

In some embodiments, an occupancy map could be quantized/downsampled byoPrecision which can be derived from the decoded occupancy map videoresolution and the nominal resolution of the decoded video frames andthen dequantized/upsampled when it is used. Therefore,(oPrecision×oPrecision) pixels will share one same value (1. Occupied 0.Empty). When the (oPrecision×oPrecision) pixels were not fully filledwith 1 before the quantization process, the dequantization process willmark previously empty pixels with redundant points, and it would add onthe distortion and complexity of the point cloud.

A method which simply discards samples that would have otherwise createdadditional points may result in holes or crack during reconstruction ofthe point cloud. A method which moves occupied samples to reduceredundant pixels may, for irregular shapes, result in redundant pixels.

In some embodiments, to improve upon such methods and to removeredundant output points, the width, height, and placement of a patch inan occupancy map may be adjusted.

Point Cloud Resampling

In some embodiments, a point cloud resampling module, such as pointcloud resampling module 252, may resample a point cloud as describedbelow.

In some embodiments, dynamic point clouds may have a different number ofpoints from one frame to another. Efficient temporal prediction mayrequire mapping the points of the current frame, denoted CF, to thepoints of a reference frame, denoted RF. Signaling such a mapping in abit stream may require a high number of bits and thus may beinefficient. Instead, re-sampling of a current frame CF may be performedso that the current frame CF has the same number of points as referenceframe RF. More precisely, the points of reference frame RF may bedisplaced such that its shape matches the shape of current frame CF. Asa second step, the color and attributes of current frame CF may betransferred to the deformed version of reference frame RF. The obtainedframe CF′ may be considered as the re-sampled version of the currentframe. The decision to compress the approximation CF′ of CF may be madeby comparing the rate distortion costs of both options (e.g., encodingCF′ as inter-frame vs. encoding CF as intra-frame). In some embodiments,pre-adjusting RF may be performed in an effort to make it a betterreference for future CF images. Resampling may comprise the following:

-   -   a. First, normals of the points associated with current frame CF        and reference frame RF may be estimated and oriented        consistently. For every point P belonging to current frame CF        (resp. Q belonging to RF), let α(P) (resp., α(Q)) be its        position and ∇(P) (resp., ∇(Q)) its normal. A 6D vector, denoted        ν(P) (resp., ν(Q)) is then associated with every point by        combining its position and a weighted version of its normal in        the same vector.

${{v(P)} = {{\begin{bmatrix}{\alpha(P)} \\{ɛ{\nabla(P)}}\end{bmatrix}{v(Q)}} = \begin{bmatrix}{\alpha(Q)} \\{ɛ{\nabla(Q)}}\end{bmatrix}}},$

-   -    where ε is a parameter controlling the importance of normal for        positions. ε could be defined by the user or could be determined        by applying an optimization procedure. They could also be fixed        of adaptive.    -   b. Two mappings from reference frame RF to current frame CF and        from current frame CF to reference frame RF are computed as        follows:        -   i. Every point Q of reference frame RF is mapped to the            point P(Q) of current frame CF that has the minimum distance            to Q in the 6D space defined in the previous step.        -   ii. Every point P of current frame CF is mapped to the point            Q(P) of reference frame RF that has the minimum distance to            P in the 6D space defined in the previous step. Let ρ(Q) be            the set of points of current frame CF that are mapped to the            same point Q.    -   c. At each iteration        -   i. The positions of the points of reference frame RF are            updated as follows:

${{\alpha^{\prime}(Q)} = {{w \cdot {\alpha\left( {P(Q)} \right)}} + {\frac{\left( {1 - w} \right)}{{\rho(Q)}}{\sum\limits_{P \in {\rho{(Q)}}}\;{\alpha(P)}}}}},$

-   -   -   where |ρ(Q)| is the number of elements of ρ(Q). The            parameter w could be defined by the user or could be            determined by applying an optimization procedure. It could            also be fixed or adaptive.        -   ii. The previous updated step results usually in an            irregular repartition of the points. In order to overcome            such limitations, a Laplacian-based smoothing procedure is            applied. The idea is to update the positions of the points            such that they stay as close as possible to {α′ (Q)}, while            favoring a repartition as close as possible to the original            point repartition in reference frame RF. More precisely, the            following sparse linear system may be solved:

${\left\{ {\alpha^{*}(Q)} \right\} = {{\arg\min}_{\{{\alpha^{\prime}{(Q)}}\}}\left\{ {{\sum\limits_{Q \in {RF}}\;{{{\alpha^{''}(Q)} - {\alpha^{\prime}(Q)}}}^{2}} + {\gamma{\sum\limits_{Q \in {RF}}{{{\alpha^{''}(Q)} - {\frac{1}{R}{\sum\limits_{Q^{\prime}{{\epsilon N}{(Q)}}}\;{\alpha^{''}\left( Q^{\prime} \right)}}} - {\alpha(Q)} - {\frac{1}{R}{\sum\limits_{Q^{\prime}{{\epsilon N}{(Q)}}}\mspace{11mu}{\alpha\left( Q^{\prime} \right)}}}}}^{2}}}} \right\}}},$

-   -   -    where N(Q) is the set of the R nearest neighbors of Q in            reference frame RF.        -   iii. The mappings between the updated RF′ point cloud and            current frame CF are then updated as follows            -   1. Every point Q of RF′ is mapped to the point P(Q) of                current frame CF that has the minimum distance to Q in                the 3D space of positions.            -   2. Every point P of current frame CF is mapped to the                point Q(P) of reference frame RF that has the minimum                distance to P in the 3D space of positions. Let ρ(Q) be                the set of points of current frame CF that are mapped to                the same point Q.

    -   d. This process is iterated until a pre-defined number of        iterations is reached or there is no further change.

    -   e. At this stage, the color and attribute information is        transferred from current frame CF to RF′ by exploiting the        following formula

${{A(Q)} = {{{w(A)} \cdot {A\left( {P(Q)} \right)}} + {\frac{\left( {1 - {w(A)}} \right)}{{\rho(Q)}}{\sum\limits_{P \in {\rho{(Q)}}}{A(P)}}}}},$

-   -    where A stands for the texture or attribute to be transferred,        |ρ(Q)| is the number of elements of ρ(Q). The parameter w(A)        could be defined by the user or could be determined by applying        an optimization procedure. It could also be fixed of adaptive.        3D Motion Compensation

In some embodiments, the positions, attributes and texture informationmay be temporally predicted by taking the difference between the valueat current resampled frame minus a corresponding value, e.g. motioncompensated value, from the reference frame. These values may be fed tothe image generation stage to be stored as images. For example, suchtechniques may be performed by 3D motion compensation and delta vectorprediction module 254.

Smoothing Filter

In some embodiments, a smoothing filter of a decoder, such as smoothingfilter 244 or smoothing filter 276 of decoder 230 or decoder 280, mayperform smoothing as described below.

In some embodiments, a reconstructed point cloud may exhibitdiscontinuities at the patch boundaries, especially at very lowbitrates. In order to alleviate such a problem, a smoothing filter maybe applied to the reconstructed point cloud. Applying the smoothingfilter may comprise:

-   -   a. By exploiting the occupancy map, both the encoder and the        decoder may be able to detect boundary points, which are defined        as being points belonging to B0×B0 blocks encoded during the        last iteration of the hierarchical occupancy map compression        procedure described in previous sections above.    -   b. The boundary points may have their        positions/attribute/texture updated. More precisely, respective        boundary points may be assigned a smoothed position based on its        R nearest neighbors in the point cloud. The smoothed position        may be the centroid/median of the nearest neighbors. Another        option may comprise fitting a plane or any smooth surface the        nearest neighbor and assigning as a smoothed position the        projection of the point on that surface. The number of        parameters and/or the smoothing strategy may be chosen by a user        or determined by applying an optimization strategy. They may be        fixed for all the points or chosen adaptively. These parameters        may be signaled in the bit stream.    -   c. In order to reduce the computational complexity of the        smoothing stage, a subsampled version of the reconstructed point        cloud may be considered when looking for the nearest neighbors.        Such subsampled version could be efficiently derived by        considering a subsampled version of the geometry image and the        occupancy map.        Closed-Loop Color Conversion

In some embodiments, an encoder and/or decoder for a point cloud mayfurther include a color conversion module to convert color attributes ofa point cloud from a first color space to a second color space. In someembodiments, color attribute information for a point cloud may be moreefficiently compressed when converted to a second color space. Forexample, FIGS. 4A and 4B illustrates encoders 400 and 450 which aresimilar encoders as illustrated in FIGS. 2A and 2C, but that furtherinclude color conversion modules 402 and 404, respectively. While notillustrated, decoders such as the decoders illustrated in FIGS. 2B and2D, may further include color conversion modules to convert colorattributes of a decompressed point cloud back into an original colorspace, in some embodiments.

FIG. 4C illustrates components of a closed-loop color conversion module,according to some embodiments. The closed-loop color conversion module410 illustrated in FIG. 4C may be a similar closed-loop color conversionmodule as closed-loop color conversion modules 402 and 404 illustratedin FIGS. 4A and 4B.

In some embodiments, a closed-loop color conversion module, such asclosed-loop color conversion module 410, receives a compressed pointcloud from a video encoder, such as video compression module 218illustrated in FIG. 4A or video compression module 264 illustrated inFIG. 4B. Additionally, a closed-loop color conversion module, such asclosed-loop color conversion module 410, may receive attributeinformation about an original non-compressed point cloud, such as colorvalues of points of the point cloud prior to being down-sampled,up-sampled, color converted, etc. Thus, a closed-loop color conversionmodule may receive a compressed version of a point cloud such as adecoder would receive and also a reference version of the point cloudbefore any distortion has been introduced into the point cloud due tosampling, compression, or color conversion.

In some embodiments, a closed-loop color conversion module, such asclosed-loop color conversion module 410, may include a videodecompression module, such as video decompression module 270, and ageometry reconstruction module, such as geometry reconstruction module412. A video decompression module may decompress one or more videoencoded image frames to result in decompressed image frames eachcomprising one or more patch images packed into the image frame. Ageometry reconstruction module, such as geometry reconstruction module412, may then generate a reconstructed point cloud geometry. Are-coloring module, such as re-coloring module 414, may then determinecolors for points in the point cloud based on the determinedreconstructed geometry. For example, in some embodiments, a nearestneighbor approach or other approach may be used to determine estimatedcolor values for points of the point cloud based on sub-sampled colorinformation, wherein a color value is not explicitly encoded for eachpoint of the point cloud. Because there may be losses during thepatching process, compression process, decompression process, andgeometry reconstruction process, the geometry of the points in thereconstructed point cloud may not be identical to the geometry in theoriginal point cloud. Due to this discrepancy, color compressiontechniques that rely on geometrical relationships between points toencode color values may result in colors that are slightly differentwhen decoded and decompressed than the original colors. For example, ifa color is to be determined based on color values of the nearestneighboring points, a change in geometry may cause a different nearestneighbor to be selected to determine the color value for the point atthe decoder than was selected to encode a residual value at the encoder.Thus distortion may be added to the decoded decompressed point cloud.

If a color space conversion module does not account for this distortionthat takes place when converting a point cloud into patches packed in animage frame and that takes place when encoding the image frames, thecolor space conversion module may select less than optimum colorconversion parameters, such as luma and chroma values. For example,optimum color conversion parameters that cause a packed image frame in afirst color space to closely match the packed image frame converted intoa second color space may be different than optimum color conversionparameters when upstream and downstream distortions are accounted for.

In order to account for such distortions, a texture/attribute imagecolor space conversion and re-sampling module, such as module 416, maytake into account a difference between the “re-created” color valuesfrom re-coloring module 416 and the original color values from theoriginal non-compressed reference point cloud when determining colorconversion parameters for converting an image frame from a first colorspace, such as R′G′B′ 4:4:4 to YCbCr 4:2:0, for example. Thus, thecolor-converted and re-sampled texture/attribute images provided tovideo encoder 218 and 264, as shown in FIG. 4C may take into accountdistortion introduced at any stage of compression and decompression of apoint cloud, and may utilize optimum color conversion parameters takinginto account such distortion.

Such methods may result in considerably reduced distortion whenreconstructing the point cloud representation, while maintaining thehigh compressibility characteristics of the 4:2:0 signal.

In some embodiments, conversion from 4:4:4 R′G′B′ to a 4:2:0 YCbCrrepresentation is performed using a 3×3 matrix conversion of the form:

$\begin{bmatrix}Y^{\prime} \\{Cb} \\{Cr}\end{bmatrix} = {\begin{bmatrix}a_{YR} & a_{YG} & a_{YB} \\a_{CbR} & a_{CbG} & a_{CbB} \\a_{CrR} & a_{CrG} & a_{CrB}\end{bmatrix}\begin{bmatrix}R^{\prime} \\G^{\prime} \\B^{\prime}\end{bmatrix}}$

In the above matrix, Y′ is the luma component and Cb and Cr are thechroma components. The values of R′, G′, and B′ correspond to the red,green, and blue components respectively, after the application of atransfer function that is used to exploit the psycho-visualcharacteristics of the signal. The coefficients a_(YR) through a_(CrB)are selected according to the relationship of the red, green, and bluecomponents to the CIE 1931 XYZ color space. Furthermore, the Cb and Crcomponents are also related to Y′ in the following manner:

${Cb} = {{\frac{B^{\prime} - Y^{\prime}}{alpha}\mspace{14mu}{with}\mspace{14mu}{alpha}}\; = \;{2 \star \left( {1 - a_{YB}} \right)}}$${Cr} = {{\frac{R^{\prime} - Y^{\prime}}{beta}\mspace{14mu}{with}\mspace{14mu}{beta}} = {2*\left( {1 - a_{YR}} \right)}}$with also the following relationships:

${a_{CbR} = {- \frac{a_{YR}}{2*\left( {1 - a_{YB}} \right)}}}{a_{CbR} = {- \frac{a_{YG}}{2*\left( {1 - a_{YB}} \right)}}}{a_{CbB} = {0.5}}{a_{CrR} = {0.5}}$$a_{CrB} = {- \frac{a_{YG}}{2*\left( {1 - a_{YR}} \right)}}$$a_{CrB} = {- \frac{a_{YB}}{2*\left( {1 - a_{YR}} \right)}}$

The process described above is followed by a 2× down-samplinghorizontally and vertically of the chroma components, resulting inchroma components that are 4 times smaller, in terms of overall numberof samples, 2× smaller horizontally and 2× smaller vertically, comparedto those of luma. Such a process can help not only with compression butalso with bandwidth and processing complexity of the YCbCr 4:2:0signals.

In using such an approach quantization for the color components, as wellas the down sampling and up sampling processes for the chromacomponents, may introduce distortion that could impact the quality ofthe reconstructed signals especially in the R′G′B′ but also in the XYZ(CIE 1931 domains). However, a closed loop conversion process, where thechroma and luma values are generated while taking into account suchdistortions, may considerably improve quality.

In a luma adjustment process, for example, the chroma components may beconverted using the above formulations, additionally a down sampling andup sampling may be performed given certain reference filteringmechanisms. Afterwards, using the reconstructed chroma samples, anappropriate luma value may be computed that would result in minimaldistortion for the luminance Y component in the CIE 1931 XYZ space. Suchluma value may be derived through a search process instead of a directcomputation method as provided above. Refinements and simplifications ofthis method may include interpolative techniques to derive the lumavalue.

Projected point cloud images can also benefit from similar strategiesfor 4:2:0 conversion. For example, closed loop color conversion,including luma adjustment methods may be utilized in this context. Thatis, instead of converting point cloud data by directly using the 3×3matrix above and averaging all neighboring chroma values to generate the4:2:0 chroma representation for the projected image, one may firstproject point cloud data/patches using the R′G′B′ representation on a4:4:4 grid. For this new image one may then convert to the YCbCr 4:2:0representation while using a closed loop optimization such as the lumaadjustment method. Assuming that the transfer characteristics functionis known, e.g. BT.709, ST 2084 (PQ), or some other transfer function aswell as the color primaries of the signal, e.g. BT.709 or BT.2020, anestimate of the luminance component Y may be computed before the finalconversion. Then the Cb and Cr components may be computed, down sampledand up sampled using more sophisticated filters. This may then befollowed with the computation of the Y′ value that would result in aluminance value Yrecon that would be as close as possible to Y. Ifdistortion in the RGB domain is of higher distortion, a Y′ value thatminimizes the distortion for R′, G′, and B′ jointly, could be consideredinstead.

For point cloud data, since geometry may also be altered due to lossycompression, texture distortion may also be impacted. In particular,overall texture distortion may be computed by first determining for eachpoint in the original and reconstructed point clouds their closest pointin the reconstructed and original point clouds respectively. Then theRGB distortion may be computed for those matched points and accumulatedacross the entire point cloud image. This means that if the geometry wasaltered due to lossy compression, the texture distortion would also beimpacted. Given that the texture may have been distorted, it may bedesirable to consider geometry during closed loop conversion of chroma.

In some embodiments, the geometry is modified so that the relativesampling density in a given region of the point cloud is adjusted to besimilar to other regions of the point cloud. Here the relative samplingdensity is defined as density of original points relative to the uniform2D sampling grid.

Because the relative sampling density can vary within a given patch,this information can be used to guide the patch decomposition process asdescribed above in regard to occupancy maps and auxiliary information,where patch approximation is used to determine local geometry.Furthermore, this information can be used to guide encoding parametersto achieve more uniform quality after compression. If a local region hashigher relative sampling density, the encoder may code that regionbetter through a variety of means. The variety of means may include:variable block size decision, Quantization Parameters (QPs),quantization rounding, de-blocking, shape adaptive offset (SAO)filtering, etc.

In some embodiments, the geometry information is first compressedaccording to a target bitrate or quality, and then it is reconstructedbefore generating the texture projected image. Then, given thereconstructed geometry, the closest point in the reconstructed pointcloud is determined that corresponds to each point in the original pointcloud. The process may be repeated for all points in the reconstructedpoint cloud by determining their matched points in the original pointcloud. It is possible that some points in the reconstructed point cloudmay match multiple points in the original point cloud, which would haveimplications in the distortion computation. This information may be usedin the closed loop/luma adjustment method so as to ensure a minimizedtexture distortion for the entire point cloud. That is, the distortionimpact to the entire point cloud of a sample Pr at position (x,y,z) inthe reconstructed point cloud can be computed (assuming the use of MSEon YCbCr data for the computation):D(Pr)=Doriginal(Pr)+Dreconstructed(Pr)D(Pr)=Sum_matching(((Y_pr−Y_or(i)){circumflex over( )}2+(Cb_pr−Cb_or(i)){circumflex over ( )}2+(Cr_pr−Cr_or(i)){circumflexover ( )}2)+sqrt((Y_pr−Y_or){circumflex over( )}2+(Cb_pr−Cb_or){circumflex over ( )}2+(Cr_pr−Cr_or){circumflex over( )}2)

In the above equation, Y_pr, Cb_pr, and Cr_pr are the luma and chromainformation of point Pr, Y_or(i), Cb_or(i), and Cr_or(i) correspond tothe luma and chroma information of all the points that were found tomatch the geometry location of point Pr from the original image, andY_or, Cb_or, and Cr_or is the point that matches the location of pointPr in the original as seen from the reconstructed image.

If the distortion computation in the context of closed loopconversion/luma adjustment utilizes D(Pr), then better performance maybe achieved since it not only optimizes projected distortion, but alsopoint cloud distortion. Such distortion may not only consider luma andchroma values, but may instead or additionally consider other colordomain components such as R, G, or B, luminance Y, CIE 1931 x and y, CIE1976 u′ and v′, YCoCg, and the ICtCp color space amongst others.

If geometry is recompressed a different optimal distortion point may bepossible. In that case, it might be appropriate to redo the conversionprocess once again.

In some embodiments, texture distortion, as measured as described below,can be minimized as follows:

-   -   Let(Q(j))_(i∈{1, . . . , N}) and (P_(rec)(i))_(i∈{1, . . . , N)        _(rec) _(}) be the original and the reconstructed geometries,        respectively.    -   Let N and N_(rec) be the number of points in the original and        the reconstructed point clouds, respectively.    -   For each point P_(rec)(i) in the reconstructed point cloud, let        Q*(i) be its nearest neighbor in the original point cloud and        R(Q*(i)), G (Q*(i)), and B (Q*(i)) the RGB values associated        with Q*(i).    -   For each point P_(rec)(i) in the reconstructed point cloud, let        ⁺(i)=(Q⁺(i, h))_(h∈{1, . . . , H(i)}) be the set of point in the        original point cloud that share P_(rec)(i) as their nearest        neighbor in the reconstructed point cloud. Note that        ⁺(i) could be empty or could have one or multiple elements.    -   If        ⁺(i) is empty, then the RGB values R(Q*(i)), G(Q*(i)), and        B(Q*(i)) are associated with the point P_(rec)(i).    -   If Q⁺(i) is not empty, then proceed as follows:        -   Virtual RGB values, denoted R(            ⁺(i)), G(            ⁺(i)), and B(            ⁺(i)), are computed as follows:

${\bullet\mspace{14mu}{R\left( {{\mathbb{Q}}^{+}(i)} \right)}} = {\frac{1}{H(i)}{\sum\limits_{h = 1}^{H{(i)}}{R\left( {Q^{+}\left( {i,h} \right)} \right)}}}$${\bullet\mspace{14mu}{G\left( {{\mathbb{Q}}^{+}(i)} \right)}} = {\frac{1}{H(i)}{\sum\limits_{h = 1}^{H{(i)}}{G\left( {Q^{+}\left( {i,h} \right)} \right)}}}$${\bullet\mspace{14mu}{B\left( {{\mathbb{Q}}^{+}(i)} \right)}} = {\frac{1}{H(i)}{\sum\limits_{h = 1}^{H{(i)}}{B\left( {Q^{+}\left( {i,h} \right)} \right)}}}$

-   -   -   Note that R(            ⁺(i)), G(            ⁺(i)), and B(            ⁺(i)) correspond to the average RGB values of the points of            ⁺(i).        -   The final RGB values R(P_(rec)(i)), G(P_(rec)(i)), and            B(P_(rec)(i)) are obtained by applying the following linear            interpolation:            R(P _(rec)(i))=wR(            ⁺(i))+(1−w)R(Q*(i))            G(P _(rec)(i))=wR(            ⁺(i))+(1−w)G(Q*(i))            B(P _(rec)(i))=wR(            ⁺(i))+(1−w)B(Q*(i))        -   The interpolation parameter w is chosen such that the            following cost function C(i) is minimized

${C(i)} = {\max\left\{ {\frac{1}{N}{\sum\limits_{h = 1}^{H{(i)}}\left\{ {{\left( {{R\left( {P_{rec}(i)} \right)} - {R\left( {{\mathbb{Q}}^{+}\left( {i,h} \right)} \right)}} \right)^{2} + \left. \quad{\left( {{G\left( {P_{rec}(i)} \right)} - {G\left( {{\mathbb{Q}}^{+}\left( {i,h} \right)} \right)}} \right)^{2} + \left( {{B\left( {P_{rec}(i)} \right)} - {B\left( {{\mathbb{Q}}^{+}\left( {i,h} \right)} \right)}} \right)^{2}} \right\}}\ ,{\frac{1}{N_{rec}}\left\{ {\left( {{R\left( {P_{rec}(i)} \right)} - {R\left( {Q^{*}(i)} \right)}} \right)^{2} + \left. \quad{\left( {{G\left( {P_{rec}(i)} \right)} - {G\left( {Q^{*}(i)} \right)}} \right)^{2} + \left( {{B\left( {P_{rec}(i)} \right)} - {B\left( {Q^{*}(i)} \right)}} \right)^{2}} \right\}} \right\}}} \right.}} \right.}$

-   -   -   Note that by minimizing the cost C(i), the distortion            measure as described below is minimized.        -   Different search strategies may be used to find the            parameter w            -   Use the closed form solution described below.            -   No search: use w=0.5.            -   Full search: choose a discrete set of values                (w_(i))_(i=1 . . . w) in the interval [0,1] and evaluate                C(i) for these values in order to find the w*, which                minimizes C(i).            -   Gradient descent search: start with w=0.5. Evaluate                E1(i), E2(i) and C(i). Store C(i) and w as the lowest                cost and its associated interpolation parameter w. If                E1(i)>E2(i), update w based on the gradient of E1(i),                else use the gradient of E2(i). Re-evaluate E1(i),                E2(i), and C(i) at the new value of w. Compare the new                cost C(i) to the lowest cost found so far. If new cost                is higher than the lowest cost stop, else update the                lowest cost and the associated value of w, and continue                the gradient descent, where R(P_(rec)(i)), G(P_(rec)                (i)), and B(P_(rec) (i)) are the three unknowns to be                determined.

In some embodiments, the above process could be performed with othercolor spaces and not necessarily the RGB color space. For example, theCIE 1931 XYZ or xyY, CIE 1976 Yu′v′, YCbCr, IPT, ICtCp, La*b*, or someother color model could be used instead. Furthermore, differentweighting of the distortion of each component could be considered.Weighting based on illumination could also be considered, e.g. weightingdistortion in dark areas more than distortion in bright areas. Othertypes of distortion, that include neighborhood information, could alsobe considered. That is, visibility of errors in a more sparse area islikely to be higher than in a more dense region, depending on theintensity of the current and neighboring samples. Such information couldbe considered in how the optimization is performed.

Down sampling and up sampling of chroma information may also considergeometry information, if available. That is, instead of down samplingand up sampling chroma information without consideration to geometry,the shape and characteristics of the point cloud around the neighborhoodof the projected sample may be considered, and appropriately consider orexclude neighboring samples during these processes. In particular,neighboring samples for down sampling or interpolating may be consideredthat have a normal that is as similar as possible to the normal of thecurrent sample. Weighting during filtering according to the normaldifference as well as distance to the point may also be considered. Thismay help improve the performance of the down sampling and up samplingprocesses.

It should be noted that for some systems, up sampling of the Cb/Crinformation may have to go through existing architectures, e.g. anexisting color format converter, and it might not be possible to performsuch guided up sampling. In those cases, only considerations for downsampling may be possible.

In some embodiments, it may be possible to indicate in the bit streamsyntax the preferred method for up sampling the chroma information. Adecoder (included in an encoder), in such a case, may try a variety ofup sampling filters or methods, find the best performing one andindicate that in the bit stream syntax. On the decoder side, the decodermay know which up sampling method would perform best for reconstructingthe full resolution YCbCr and consequently RGB data. Such method couldbe mandatory, but could also be optional in some architectures.

Clipping as well as other considerations for color conversion, may alsoapply to point cloud data and may be considered to further improve theperformance of the point cloud compression system. Such methods may alsoapply to other color representations and not necessarily YCbCr data,such as the YCoCg and ICtCp representation. For such representationsdifferent optimization may be required due to the nature of the colortransform.

Example Objective Evaluation Method

A point cloud consists of a set of points represented by (x,y,z) andvarious attributes of which color components (y,u,v) are of importance.First, define the point v. It has as a mandatory position in a 3D space(x,y,z) and an optional color attribute c that has components r,g,b ory,u,v and optional other attributes possibly representing normal ortexture mappings.point v=(((x,y,z),[c],[a ₀ . . . a _(A)]):x,y,z∈R,[c∈(r,g,b)|r,g,b∈N],[a _(i)∈[0,1]])  (def. 1)

The point cloud is then a set of K points without a strict ordering:Original Point Cloud V _(or)={(v _(i)): i=0 . . . K−1}  (def. 2)

The point cloud comprises a set of (x,y,z) coordinates and attributesthat can be attached to the points. The original point cloud Vor (420)will act as the reference for determining the quality of a seconddegraded point cloud Vdeg (424). Vdeg consists of N points, where N doesnot necessarily=K. Vdeg is a version of the point cloud with a lowerquality possibly resulting from lossy encoding and decoding of Vor (e.g.operation 422). This can result in a different point count N.Degraded Point Cloud V _(deg)={(v _(i)): i=0 . . . N−1}  (def. 3)

The quality metric Q_(point cloud) is computed from Vor and Vdeg andused for assessment as shown in FIG. 4D for full reference qualitymetric 426.

Table 3, below, outlines the metrics used for the assessment of thequality of a point cloud, in some embodiments. The geometric distortionmetrics are similar as ones used for meshes based on haussdorf (Linf)and root mean square (L2), instead of distance to surface. This approachtakes the distance to the closest/most nearby point in the point cloud(see definitions 4, 5, 6, and 7) into account. Peak signal to noiseratio (PSNR) is defined as the peak signal of the geometry over thesymmetric Root Mean Square (RMS/rms) distortion (def 8.). For colors, asimilar metric is defined; the color of the original cloud is comparedto the most nearby color in the degraded cloud and peak signal to noiseratio (PSNR) is computed per YUV/YCbCr component in the YUV color space(def. 10). An advantage of this metric is that it corresponds to peaksignal to noise ratio (PSNR) in Video Coding. The quality metric issupported in the 3DG PCC software.

TABLE 3 Assessment criteria for assessment of the point cloud quality ofVdeg, Q_(point)_cloud d_symmetric_rms Symmetric rms distance between thepoint clouds (def. 5.) d_symmetric_haussdorf Symmetric haussdorfdistance between the clouds (def. 7.) psnr_geom Peak signal to noiseratio geometry (vertex positions) (def. 8.) psnr_y Peak signal to noiseratio geometry (colors Y) (def. 10) psnr_u Peak signal to noise ratiogeometry (colors U) (as def. 10 rep. y for u) psnr_v Peak signal tonoise ratio geometry (colors V) (as def. 10 rep. y for v)${d_{{rm}\; s}\left( {V_{or},V_{\deg}} \right)} = \sqrt{{\frac{1}{K}{\sum\limits_{{vo} \in {Vor}}{〚{{vo} - {{{vd}{\_ nearest}}{\_ neighbour}}}〛}^{2}}}\;}$(def. 4) d_(symmetric)_rms(V_(or), V_(deg)) = max(d_(rms)(V_(or),V_(deg)), d_(rms)(V_(deg), V_(or))) (def.5) d_(haussdorf)(V_(or),V_(deg)) = max_(v) _(o) _(ϵV) _(or) ,(∥v_(o) −v_(d)_nearest_neighbour∥₂, v_(d) is the point in Vdeg closest (def.6) tov_(o) (L2)) d_(symmetric)_haussdorf(V_(or), V_(deg)) =max(d_(haussdorf)(V_(or), V_(deg)), d_(haussdorf)(V_(deg), V_(or)) (def.7) BBwidth = max((xmax − xmin), (ymax − ymin), (zmax − zmin) (def. 8)psnr_(geom) = 10log₁₀(|BBwidth∥₂ ²/(d_(symmetricrms)(V))²) (def. 9)${d_{y}\left( {V_{or},V_{\deg}} \right)} = \sqrt{{\frac{1}{K}{\sum\limits_{{vo} \in {Vor}}{〚{{y({vo})} - {y\left( v_{{dnearest}_{neighbour}} \right)}}〛}^{2}}}\;}$(def. 10) psnr_(y) = 10log₁₀(|255∥²/(d_(y)(V_(or), V_(deg))²) (def. 11)

In some embodiments, additional metrics that define the performance of acodec are outlined below in Table 4.

TABLE 4 Additional Performance Metrics Compressed size Completecompressed mesh size In point count K, the number of vertices in Vor Outpoint count N, number of vertices in Vdeg Bytes_geometry_layer Number ofbytes for encoding the vertex positions Bytes_color_layer (opt) Numberof bytes for encoding the colour attributes Bytes_att_layer (opt) Numberof bytes for encoding the other attributes Encoder time (opt) Encodertime in ms on commodity hardware (optional) Decoder time (opt) Decodertime in ms on commodity hardware (optional)Example Closed Form Solution

For each point P_(rec)(i) in the reconstructed point cloud, let Q*(i) beits nearest neighbor in the original point cloud. For each pointP_(rec)(i) in the reconstructed point cloud, let (Q⁺(i,h))_(h∈{1, . . . , H(i)}) be the set of point in the original pointcloud that share P_(rec) (i) as their nearest neighbor in thereconstructed point cloud. Let

⁺(i) be the centroid of (Q⁺(i, h))_(h∈{1, . . . , H(i)}).If H=0, then C(P _(rec)(i))=C(Q*(i))

Denote as R-G-B vector C(P) associated with a given point P. In order tocompute the color for a given P_(rec)(i) we have the followingformulation:

$\underset{C{({P_{rec}{(i)}})}}{\arg\;\min}\mspace{14mu}\max\left\{ {{\frac{1}{N_{rec}}{{{C\left( {P_{rec}(i)} \right)} - {C\left( {Q^{*}(i)} \right)}}}^{2}},{\frac{1}{N}{\sum\limits_{h = 1}^{H}\;{{{C\left( {P_{rec}(i)} \right)} - {C\left( {{\mathbb{Q}}^{+}\left( {i,h} \right)} \right)}}}^{2}}}} \right\}$${{Where}\mspace{14mu}\max\left\{ {{\frac{1}{N_{rec}}{{{C\left( {P_{rec}(i)} \right)} - {C\left( {Q^{*}(i)} \right)}}}^{2}},{\sum\limits_{h = 1}^{H}{{{C\left( {P_{rec}(i)} \right)} - {C\left( {{\mathbb{Q}}^{+}(i)} \right)} + {C\left( {{\mathbb{Q}}^{+}(i)} \right)} - {C\left( {Q^{+}\left( {i,h} \right)} \right)}}}^{2}}} \right\}} = {{\max\left\{ {{\frac{1}{N_{rec}}{{{C\left( {P_{rec}(i)} \right)} - {C\left( {Q^{*}(i)} \right)}}}^{2}},{{\frac{H}{N}{{{C\left( {P_{rec}(i)} \right)} - {C\left( {{\mathbb{Q}}^{+}(i)} \right)}}}^{2}} + {\frac{1}{N}{\sum\limits_{h = 1}^{H}{{{C\left( {{\mathbb{Q}}^{+}(i)} \right)} - {C\left( {Q^{+}\left( {i,h} \right)} \right)}}}^{2}}} + {\frac{2}{N}{\sum\limits_{h = 1}^{H}\left\langle {{{C\left( {P_{\tau ec}(i)} \right)} - {C\left( {{\mathbb{Q}}^{+}(i)} \right)}},\ {{C\left( {{\mathbb{Q}}^{+}(i)} \right)} - {C\left( {Q^{+}\left( {i,h} \right)} \right)}}} \right\rangle}}}} \right\}} = {\max\left\{ {{\frac{1}{N_{rec}}{{{C\left( {P_{rec}(i)} \right)} - {C\left( {Q^{*}(i)} \right)}}}^{2}},{{\frac{H}{N}{{{C\left( {P_{rec}(i)} \right)} - {C\left( {{\mathbb{Q}}^{+}(i)} \right)}}}^{2}} + {\frac{1}{N}{\sum\limits_{h = 1}^{H}{{{C\left( {{\mathbb{Q}}^{+}(i)} \right)} - {C\left( {Q^{+}\left( {i,h} \right)} \right)}}}^{2}}}}} \right\}}}$

Now denote

${D^{2} = {\sum\limits_{h = 1}^{H}\;{{{C\left( {{\mathbb{Q}}^{+}(i)} \right)} - {C\left( {Q^{+}\left( {i,h} \right)} \right)}}}^{2}}},$so that

$\underset{C{({P_{rec}{(i)}})}}{\arg\;\min}\mspace{14mu}\max{\left\{ {{\frac{1}{N_{rec}}{{{C\left( {P_{rec}(i)} \right)} - {C\left( {Q^{*}(i)} \right)}}}^{2}},{{\frac{H}{N}{{{C\left( {P_{rec}(i)} \right)} - {C\left( {{\mathbb{Q}}^{+}(i)} \right)}}}^{2}} + \frac{D^{2}}{N}}} \right\}.}$Note: if H=1 then D ²=0

Let C⁰(P_(rec)(i)) be a solution of the previous minimization problem.It can be shown that C⁰(P_(rec)(i)) could be expressed as:C ⁰(P _(rec)(i))=wC(Q*(i))+(1−w)C(

⁺(i))

Furthermore, C⁰(P_(rec)(i)) verifies:

${\frac{1}{N_{rec}}{{{{wC}\left( {Q^{*}(i)} \right)} + {\left( {1 - w} \right){C\left( {{\mathbb{Q}}^{+}(i)} \right)}} - {C\left( {Q^{*}(i)} \right)}}}^{2}} = {{{\frac{H}{N}{{{w{C\left( {Q^{*}(i)} \right)}} + {\left( {1 - w} \right){C\left( {{\mathbb{Q}}^{+}(i)} \right)}} - {C\left( {{\mathbb{Q}}^{+}(i)} \right)}}}^{2}} + {\frac{D^{2}}{N}\left( {1 - w} \right)^{2}{{{C\left( {{\mathbb{Q}}^{+}(i)} \right)} - {C\left( {Q^{*}(i)} \right)}}}^{2}}} = {w^{2}\frac{HN_{rec}}{N}{{{C\left( {Q^{*}(i)} \right)} - {C\left( {{\mathbb{Q}}^{+}(i)} \right.}^{2} + \frac{D^{2}N_{rec}}{N}}}}}$Let δ² =∥C(Q*(i))−C(

⁺(i)∥² and

$r = \frac{N_{rec}}{N}$If δ²=0, then C(P _(rec)(i))=C(Q*(i))=C(

⁺(i)(1−w)²δ² =w ² rHδ ² +rD ²δ² +w ²δ²−2wδ ² =w ² rHδ ² +rD ²δ²(1−rH)w ²−2δ² w+(δ² −rD ²)=0(rH−1)w ²+2w+(αr−1)=0With

$\alpha = \frac{D^{2}}{\delta^{2}}$if H=1, then

$w = \frac{1}{2}$if H>1Δ=4−4(rH−1)(αr−1)Δ=4−4(rH−1)αr+4H−4Δ=4(H−(rH−1)αr)If Δ=0

$w = \frac{- 1}{\left( {{rH} - 1} \right)}$If Δ>0

${{w1} = \frac{{- 1} - \sqrt{\left( {H - {\left( {{Hr} - 1} \right)\alpha r}} \right)}}{\left( {{rH} - 1} \right)}}{{w2} = \frac{{- 1} + \sqrt{\left( {H - {\left( {{Hr} - 1} \right)\alpha r}} \right)}}{\left( {{rH} - 1} \right)}}$

Where the cost C(i) is computed for both w1 and w2 and the value thatleads to the minimum cost is retained as the final solution.

Compression/Decompression Using Multiple Resolutions

FIG. 5A illustrates components of an encoder that includes geometry,texture, and/or attribute downscaling, according to some embodiments.Any of the encoders described herein may further include a spatialdown-scaler component 502, a texture down-scaler component 504, and/oran attribute down-scaler component 506 as shown for encoder 500 in FIG.5A. For example, encoder 200 illustrated in FIG. 2A may further includedownscaling components as described in FIG. 5A. In some embodiments,encoder 250 may further include downscaling components as described inFIG. 5A.

In some embodiments, an encoder that includes downscaling components,such as geometry down-scaler 502, texture down-scaler 504, and/orattribute down-scaler 506, may further include a geometry up-scaler,such as spatial up-scaler 508, and a smoothing filter, such as smoothingfilter 510. In some embodiments, a reconstructed geometry image isgenerated from compressed patch images, compressed by video compressionmodule 218. In some embodiments an encoder may further include ageometry reconstruction module (not shown) to generate the reconstructedgeometry image. The reconstructed geometry image may be used by theoccupancy map to encode and/or improve encoding of an occupancy map thatindicates patch locations for patches included in one or more frameimages. Additionally, the reconstructed geometry image may be providedto a geometry up-scaler, such as geometry up-scaler 508. A geometryup-scaler may scale the reconstructed geometry image up to an originalresolution or a higher resolution approximating the original resolutionof the geometry image, wherein the original resolution is a resolutionprior to downscaling being performed at geometry down-scaler 502. Insome embodiments, the upscaled reconstructed geometry image may beprovided to a smoothing filter that generates a smoothed image of thereconstructed and upscaled geometry image. This information may then beprovided to the spatial image generation module 210, texture imagegeneration module 212, and/or the attribute image generation module 214.These modules may adjust generation of spatial images, texture images,and/or other attribute images based on the reconstructed geometryimages. For example, if a patch shape (e.g. geometry) is slightlydistorted during the downscaling, encoding, decoding, and upscalingprocess, these changes may be taken into account when generating spatialimages, texture images, and/or other attribute images to correct for thechanges in patch shape (e.g. distortion).

FIG. 5B illustrates components of a decoder 550 that includes geometry,texture, and/or other attribute upscaling, according to someembodiments. For example, decoder 550 includes texture up-scaler 552,attribute up-scaler 554, and spatial up-scaler 556. Any of the decodersdescribed herein may further include a texture up-scaler component 552,an attribute up-scaler component 554, and/or a spatial image up-scalercomponent 556 as shown for decoder 550 in FIG. 5B.

FIG. 5C illustrates rescaling from the perspective of an encoder,according to some embodiments. In some embodiments, a point cloud may bescaled in both the point cloud domain (e.g. prior to patch projection)and in a video level domain (e.g. by scaling image frames comprisingpatch information). For example FIG. 5C illustrates a point cloud 508 ofa woman. An encoder, such as encoder 500, performs 3D scaling of thepoint cloud 508 in the point cloud domain to generate a downscaled pointcloud 510. Patches generated based on downscaled point cloud 510 arepacked into image frame 512. Additionally, downscaling is performed onthe image frame 512 at the video level to reduce a resolution of theimage frame. The additional downscaling results in a downscaled imageframe 514 that is then encoded into a bit stream 516.

FIG. 5D illustrates rescaling from the perspective of a decoder,according to some embodiments. In some embodiments, a decoder, such asdecoder 550, may receive a bit stream, such as bit stream 516. Thedecoder may decode the video encoded bit stream to generate one or morevideo image frames, such as image frame 518. The decoder may furtherupscale the image frame 518 to generate an upscaled image frame 520. Thedecoder may then use a patch projection method, as described above, togenerate a reconstructed point cloud 522 from the patch informationincluded in the upscaled image frame 520. The decoder may also performscaling in the 3D point cloud domain to scale up the reconstructed pointcloud 522 to a similar size as the original point cloud. This processmay result in an upscaled reconstructed point cloud 524.

FIG. 5E illustrates an example open loop rescaling, according to someembodiments. In an open loop rescaling of an image frame, a geometryplane, and a texture or other attribute plane may be independentlyscaled, where geometry distortion is not taken into account when scalingthe texture or other attribute information. For example, geometry imageframe 526 may indicate depths of points of a point cloud relative to aprojection plane and texture or attribute image frame 528 may representrespective attributes of the points of the point cloud projected on tothe projection plane. As shown in FIG. 5E, in an open loop rescalingprocess, the geometry information and the attribute information may beindependently scaled to generate downscaled geometry image frame 532 anddownscaled attribute image frame 532, respectively. Also, as shown inFIG. 5E, the downscaled geometry image frame 530 may beencoded/compressed to generate a geometry bit stream and the downscaledattribute image frame 532 may be encoded/compressed to generate anattribute bit stream, such as a texture attribute bit stream. Forexample, spatial down-scaler 502 may downscale the geometry image frame526 and the texture down-scaler 504 may independently downscale thetexture image frame 528. In some embodiments, attribute down-scaler 506may downscale an attribute independently of spatial down-scaler 502 andtexture down-scaler 504. Because different down-scalers are used todownscale different types of image frames (e.g. spatial information,texture, other attributes, etc.), different downscaling parameters maybe applied to the different types of image frames to downscale geometrydifferent than texture or attributes.

FIG. 5F illustrates an example closed loop rescaling, according to someembodiments. In some embodiments, a closed loop rescaling process may beused by an encoder such as encoder 500 to determine distortion or otherchanges to geometry that may occur as part of a downscaling, encoding,decoding, and/or upscaling process. In some embodiments, such distortionmay be accounted for when downscaling other attributes, such as texture.An encoder, such as encoder 500, receives a point cloud 534. The encodergenerates a geometry image frame for the point cloud 534, for example animage frame comprising patches representing relative depths of thepoints. A point cloud compression geometry mapper, which may include adecomposition into patches module 506, a packing module 208, and aspatial image generation module 210, etc., generates geometry frameimage 536. A geometry down-scaler, such as spatial down-scaler 502downscales the geometry plane to generate downscaled geometry plane 538.Note that “geometry plane” is used to refer to geometry patchinformation, which may be included in an image frame only consisting ofgeometry patches as shown in FIG. 5F.

The downscaled geometry plane 538 is compressed, for example by videocompression module 218, and is converted into a geometry bit stream. Ina closed loop process as shown in FIG. 5F, the geometry bit stream isdecompressed at the encoder to generate a reconstructed geometry plane540. The reconstructed geometry plane is then upscaled, at the encoder,to generate an upscaled reconstructed geometry plane 542.

The texture points of the point cloud 534 are then mapped to the pointsof the reconstructed upscaled geometry plane 542. In this way, thetexture points are mapped to the same points in the same locations asthe decoder will encounter when reconstructing and upscaling thegeometry plane. Then, the decoder can take into account the distortionof the geometry plane that may occur due to downscaling, encoding,decoding, and upscaling.

The texture points mapped to the points of the reconstructed upscaledgeometry plane 542 may generate a texture plane 544. The texture planemay then be downscaled to generate a downscaled texture plane 546. Thedownscaled texture plane 546 may then be encoded and transmitted as atexture bit stream.

FIG. 5G illustrates an example closed loop rescaling with multipleattribute layers, according to some embodiments. In some embodiments, asimilar process as described for FIG. 5F may be followed. However,multiple degrees of downsampling may be performed for one or moreattribute planes being downscaled. For example texture attribute plane544 may not be downscaled at all (e.g. compression rate target 0), ormay be downscaled according to a plurality of compression rate targets(e.g. compression rate targets 1-4). In such embodiments, a compressionrate target may be dynamically adjusted, for example based on networkconditions, processing capacity, etc.

FIG. 5H illustrates an example of video level spatiotemporal scaling,according to some embodiments. In some embodiments, a similar process asdescribed in FIGS. 5C and 5D may be performed using video levelspatiotemporal downscaling and upscaling. For example, a frame rate(e.g. a number of frames processed per unit time) may be adjusted up ordown in order to improve compression efficiency. In such embodimentsspatial temporal adjustment may be made instead of resolution scalingand/or in addition to resolution scaling.

FIG. 5I illustrates an example closed loop rescaling with spatiotemporalscaling, according to some embodiments.

As discussed above, methods of compressing point cloud video data mayuse conventional video codecs as well as auxiliary information that canhelp describe and reconstruct the point cloud information. The encoderand decoder diagrams of how that process is performed is shown in atleast FIGS. 5A and 5B, respectively. As can be seen, the processsegments the point cloud frame into multiple 2D projected images/videos,each representing different types of information. This process isperformed by segmenting the point cloud first into multiple patches thatpermit one to efficiently project the entire 3D space data onto 2Dplanes. Each patch is associated with information such as geometry (alsoreferred to herein as “spatial information”), texture, and otherattributes if they are available. Such information is then copied at theco-located locations of the image frames on separate image sequenceswith each now containing only the geometry information, the textureinformation, and any other remaining attributes respectively. Auxiliaryinformation that contains the patch information as well as an occupancymap that dictates which areas in these projected image sequencescorrespond to actual point cloud data and which are unoccupied, e.g. maycontain no data or dummy data, are also provided. Compression is thenapplied on such information using different strategies. Auxiliaryinformation, for example, is entropy coded, while occupancy maps may bedownconverted and encoded using either conventional codecs or othermethods such as run length compression. The separate projected imagesequences on the other hand are compressed using conventional codecs.This results in a collection of multiple sub streams, e.g. a geometrysub stream, texture and attribute sub streams, as well as occupancy andauxiliary information sub streams.

As described above, all sub streams except the occupancy map areexpected to be of the same resolution. Each point in the geometry substream essentially corresponds to a point in the final 3D reconstructedpoint cloud. In some embodiments, it is permitted for the signal to beencoded at a different resolution than the original representation.Also, in some embodiments, offsetting as well as rotating the pointcloud is also possible. Seeing things from the encoder perspective, thisis done by signaling in the stream header additional metadata that wouldidentify the scaling, offset, and rotation that should be applied ontothe original point cloud data prior to projecting it onto the targetvideo planes. From the decoder perspective, these parameters are usedafter the reconstruction of a first 3D point cloud representation andutilized to generate the final 3D point cloud representation. In such ascheme, both geometry and attribute/texture video data are signaled atthe same resolution as specified in the point cloud header. Per patchmetadata including scaling factors and rotation parameters are alsosupported in such a scheme, with scaling though now applied on eachprojected patch independently.

However, in some embodiments, this scheme may be further extended byproviding additional resolution scaling flexibility in the encodedstreams. In particular, not only may the scaling be applied in 3D spaceor per patch, in some embodiments scheme scaling may be applied on theentire projected point cloud video data. This may permit use of“conventional” 2D rescaling schemes and architectures, which are readilyavailable in many architectures. Furthermore, unlike a scheme wheregeometry and attribute sub streams are encoded at the same resolution,this alternative scheme permits signaling of these sub streams atdifferent resolutions. In some embodiments, this scheme could also becombined with the 3D scaling scheme described above, e.g. the specified2D image frame scaling can follow in encoding order and precede indecoding order the 3D scaling process as described above. This canprovide further flexibility in coding performance.

In particular, with the scheme described above we know the scalingfactors, if any, that were applied to the point cloud signal in 3D spaceto change its resolution. Essentially the point cloud scene/object thatis being represented would change from resolution W_(3D)×H_(3D)×D_(3D)to (s_(x)×W_(3D))×(s_(y)×H_(3D))×(s_(z)×D_(3D)). Then this rescaledobject would be projected using the patch approach specified above intoa variety of sub videos, e.g. occupancy, geometry and attribute subvideos, each of a nominal resolution of W_(N)×H_(N). The nominalresolution may be currently specified in the group of frames headersyntax of the MPEG PCC TMC2 draft (v1.2), using the syntax elementsframe width and frame_height. The scaling factors may be added into thissyntax.

Group of frames header syntax in TMC 2 v1.2 Descriptorgroup_of_frames_header( ) {  group_of_frames_size u(8)  frame_widthu(16)  frame_height u(16)  occupancy_resolution u(8) radius_to_smoothing u(8)  neighbor_count_smoothing u(8) radius_to_boundary_detection u(8)  threshold_smoothing u(8) }

With the proposed method one may also now rescale the geometry andattribute signal i further at a resolution of W_(G)×H_(G) andW_(A(i))×H_(A(i)) respectively. There is no need of signaling theresolution of these videos in the point cloud compression PCC headerssince that information already exists in the video bit streamsthemselves. Conventional algorithms can be used to rescale the videosfrom the nominal resolution of W_(N)×H_(N) to W_(G)×H_(G) orW_(A(i))×H_(A(i)) and vice versa. These can be seen from the encoderperspective in FIG. 5C and from the decoder perspective in FIG. 5D.

FIG. 5E shows an open loop architecture for converting the geometry andattribute signals. In this architecture the geometry and attributesignals are created and converted independently. In some embodiments,the only dependency is that the geometry signal prior to downscaling andcompression is used for generating the texture plane signal. However, inFIG. 5F, a closed loop architecture is considered. In this architecture,the geometry signal is scaled and coded first, then it is reconstructedand up converted to its original resolution. This new geometry signal isthen used to generate the texture/attribute plane. Using this method,the texture (or attributes) generated more accurately correspond to thereconstructed geometry compared to the open loop architecture in theprevious figure (FIG. 5E). It should be noted that the upscaling processof the geometry, if needed, should be matched across decoders to achievethe desired closed loop performance. If the up-scalers do not match,there could be some difference in performance. The resolutions of thegeometry and attribute signals also do not need to match in any of thesesystems. Conventional up-scalers, such as a separable filter basedup-scaler, e.g. bicubic, lanczos, windowed cosine or sine, etc., or morecomplicated up-scalers, including bilateral up-scalers, edge adaptive,motion compensated, etc. could be used. Downscaling also could usesimilar methods, e.g. separable filters, edge preserving downscalers,etc.

Such an approach could also be utilized by adaptive streaming solutionsas well. In particular in adaptive streaming systems multiple streamsmay be generated at different resolutions and bitrates to better supportthe variability of a network. In this system apart from adjustingbitrates for the different layers, different resolutions can also beused between the texture and geometry to also augment suchfunctionality. An example is shown in FIG. 5G where for a particulargeometry signal multiple different bit streams are generated for thetexture/attribute layer, each potentially having different resolution aswell. A decoder may select to use the particular texture layer and thenalso select the appropriate corresponding sub-bit stream for the texturegiven the overall bandwidth characteristics of their network.

In a different aspect, downscaling and upscaling of the different layerscan be performed by considering the characteristics of the point cloudsystem and how the images are constructed. In particular, in the systemsdescribed above, images are constructed using patches. These patches areavailable at both the encoder and decoder. A conventional system likelywill not be able to consider the patches, however a more advanced systemcould utilize the patches to improve these two processes. In particular,better performance could be achieved by processing/filtering whenupscaling or downscaling only the samples within a patch. Samplesoutside the patch are likely padded samples that may have no directrelationship with the samples inside the patch and if filtered/processedtogether could contaminate the signal. By isolating such samples thiscontamination can be avoided and performance can be improved. Even ifnot able to extract the full patch location information in a system, itcould still be possible to consider other related information such asthe occupancy map information. Occupancy maps, even though less accurateif they were down sampled, can still provide some improvement inperformance. On the other hand, the interpolation process for theattribute signals may be augmented by also considering thecharacteristics, e.g. edges, in the geometry signal. In particular,edges may be extracted in the geometry signal, and they may be relatedto edges in the attribute signals and a guided interpolation based onthat information may be performed. This is possible since edges in thetwo layers are highly related, especially at the boundaries of everypatch.

In some embodiments, spatiotemporal rescaling may be applied as shown inFIGS. 5H and 5I. In some embodiments, on the encoder, frame dropping maybe performed, while the decision to drop a frame may be based on howsimilar are the “neighboring” frames or the frame dropping could be doneat a fixed rate (time stamps would still exist in the stream to informthe use of temporal relationships). In some embodiments“blending”/averaging of frames may be performed. That is, all frames maybe scaled using different phase that is controlled “temporally”. Forexample, odd frames may be scaled vertically using phase 0, while evenframes would be scaled vertically using phase 1. Those scaled images arecalled fields, which are then interleaved together to create interlaceframes. This process could be used for projected point cloud data. Notethat interleaving does not need to be restricted in vertical fashion,but could be done horizontally instead or as well.

For the temporal up conversion, frame repeats could be used, or moreintelligent methods could be used that include motion adaptive andmotion compensated strategies for the interpolation. Machine learningmethods could also be used to assist with the interpolation.

In some embodiments, assume that the temporal resolution is only reducedin one of the planes (e.g. geometry or texture) while for the other allframes are retained (along with the occupancy and patch information). Insuch a case the “shape” and location of all patches in the reducedtemporal resolution plane are known, but the exact value of itscharacteristics (depth for geometry, color for texture) is not known.However, that value may be computed by trying to locate each patch intheir temporal adjacent neighbors. That can be done by searching usingthe shape information of the patch and/or the available values of thepatch characteristics of the full resolution plane. That basically wouldinvolve a search (e.g. a motion estimation based search). When a patchis located, the information from the characteristics to interpolate fromits temporal neighbors can be copied/blended and used as a predictor.

Pre Video Compression Pre-Processing and Post Video DecompressionFiltering/Post-Processing

As discussed above, video point cloud data may be compressed usingconventional video codecs. As discussed above, the process segments apoint cloud frame into multiple 2D projected images/videos, eachrepresenting different types of information. This process is performedby segmenting the point cloud into multiple patches that permit one toefficiently project the 3D space data of the point cloud onto 2D planes.Each patch is associated with information such as geometry, texture,and/or other attributes. Such information is then copied at co-locatedlocations on separate image frame sequences with each image framecontaining the geometry information, the texture information, or anyother remaining attributes, respectively as patch images packed into theimage frame. Auxiliary information that contains the patch informationas well as an occupancy map that dictates which areas in these projectedimage frame sequences correspond to actual point cloud data and whichare unoccupied, e.g. may contain no or dummy data, are also provided.Compression is applied on such information using different strategies.Auxiliary information, for example, can be entropy coded, whileoccupancy maps may be down converted and encoded using eitherconventional video codecs or other methods such as run lengthcompression. The separate projected image sequences on the other handare compressed using conventional video codecs. This results in acollection of multiple sub streams, e.g. a geometry sub stream, textureand attribute sub streams, as well as occupancy and auxiliaryinformation sub streams. All these streams are multiplexed together togenerate the final point cloud bit stream as shown in FIG. 2A. In someembodiments, these streams may be provided to an image framepre-processing/filtering element of an encoder to make intelligentpre-processing decisions and/or to intelligently adjust parameters usedfor video encoding based on information included in these streams.

FIG. 6A illustrates components of an encoder that further includespre-video compression texture processing and/or filtering and pre-videodecompression geometry processing/filtering, according to someembodiments. Encoder 600 includes image frame pre-processing/filteringelement 602. In some embodiments, any of the encoders described herein,such as encoder 200, 250, 400, 450, or encoder 500 may further include atexture processing/filtering element, such as texture processing/filterelement 602. In some embodiments, any of the decoders described herein,such as decoder 230 or decoder 280, may further include a geometryprocessing/filtering element, such as geometry/filtering element 604. Insome embodiments, pre-processing of a packed image frame from imageframe padding element 216 may be pre-processed via image framepre-processing/filtering element 602 prior to being video encoded byvideo compression 218. In some embodiments, image framepre-processing/filtering element 602 may receive occupancy mapinformation, spatial information, for example from spatial imagegeneration element 210, texture information, for example from textureimage generation element 212, and attribute information, for examplefrom attribute image generation element 214.

In some embodiments, an image frame pre-processing/filter element, suchas image frame pre-processing/filter element 602, may determinerelationship information indicating relationships between respectiveattribute patch images and/or depth patch images. Additionally, therelationship information may include relationships between therespective patches and an image frame, for example which portions of theimage frame are occupied or unoccupied and where edges of the patches inthe image frame are located.

In some embodiments, an encoder, such as encoder 600, may utilize areceived or determined occupancy map to determine the relationshipinformation. In some embodiments, an image framepre-processing/filtering element, such as image framepre-processing/filtering element 602, may cause one or more encodingparameters of a video encoding component, such as video compression 218,to be adjusted based on the determined relationship information. Forexample, the image frame pre-processing/filtering element may cause thevideo encoding component to allocate more encoding resources to encodingportions of the image frame that are occupied with patches and may causethe video encoding component to allocate fewer encoding resources toencoding portions of the image frame that are unoccupied. Also, in someembodiments, the relationship information may indicate a plurality ofpatches (e.g. attribute patch images and/or depth patch images) thatcorrespond to a same set of points projected onto a same patch plane. Insome embodiments, an image frame pre-processing/filtering element maycause a video encoding component to utilize complementary encodingparameters when encoding patches corresponding to a same set of pointsin the point cloud.

In some embodiments, an image pre-processing/filtering element, such asimage pre-processing/filtering element 602, may determine a pooleddistortion for a set of points. For example, the image framepre-processing/filtering element may determine an amount of distortionintroduced due to video compression of a depth patch image, an amount ofdistortion introduced due to video compression of an attribute patchimage (such as colors), and an impact of overall or pooled distortion onthe reconstructed point cloud based on the interactions of thedistortions introduced for the separate patches. For example, depthdistortion may cause additional color distortion in a reconstructedpoint cloud. In some embodiments, the image framepre-processing/filtering element may adjust one or more video encodingparameters of a video encoding component, such as video compression 218,in order to reduce the overall pooled distortion for the set of points.

In some embodiments, an image pre-processing/filtering element, such asimage pre-processing/filtering element 602, may determine edges ofpatches based on the relationship information. The imagepre-processing/filtering element may further adjust one or morepre-processing processes such as color down-sampling of the one or moreimages, for example as described above in regard to closed-loop colorconversion, based on the boundaries of the patches according to theirdetermined edges.

FIG. 6B illustrates components of a decoder that further includes postvideo decompression texture processing and/or filtering and post videodecompression geometry processing/filtering, according to someembodiments. Decoder 610 includes texture processing/filtering element612 and geometry processing/filter element 614. In some embodiments, anyof the decoders described herein, such as decoder 230, 280, or 550 mayfurther include a texture processing/filtering element, such as textureprocessing/filter element 612. In some embodiments, any of the decodersdescribed herein, such as decoder 230, 280, or 550, may further includea geometry processing/filtering element, such as geometry/filteringelement 614.

In some embodiments, relationship information for patch images in animage frame may be included in or derived from a bit stream for acompressed point cloud. For example, FIG. 6C illustrates a bit streamstructure for a compressed point cloud, according to some embodiments.In some embodiments, the auxiliary information may include relationshipinformation for patch images. Also, in some embodiments an occupancy mapmay include relationship information for patches of an image frame. Forexample, an occupancy map may indicate which portions of an image frameare occupied or unoccupied. Also, the auxiliary information may indicatewhich blocks of an image frame correspond to which patches. Thisinformation may be used to determine portions of an image frame thatcorrespond to a same patch. Also depth information included in thegeometry information (e.g. depth patch images) may be used to identifyportions of image frames for points having a common depth in the pointcloud. Additionally, attribute/texture information included in thetexture video stream may be used to identify patches in the image frameswith similar textures or attribute values. In some embodiments, anattribute texture processing/filtering element, such as attributeprocessing/filtering element 612, and a geometry processing/filteringelement, such as geometry/filtering element 614, may utilize these datastreams to determine or receive relationship information for patchesincluded in one or more video encoded image frames, such as are includedin the group of frames for the geometry video stream, and such as areincluded in the group of frames for the texture/attribute video stream.The attribute processing/filtering element and/or geometryprocessing/filtering element may further utilize these determined orreceived relationships to intelligently post-process geometry imageframes in the geometry video stream or to post-process texture/attributeimage frames in the texture video stream. Also, in some embodiments,geometry patches (e.g. depth patches) and texture or attribute patchesmay be packed into a same image frame. Thus, in some embodiments, atexture/attribute video stream and a geometry video stream may becombined into a common video stream comprising image frames with bothgeometry patches and attribute/texture patches.

One of the characteristics of this point cloud coding scheme is that thedifferent projected image sequences can be not only compressed using“conventional” codecs but also processed with conventional processingalgorithms reserved for 2D image/video data. That is, one could applyde-noising, scaling, enhancement, and/or other algorithms commonly usedfor processing 2D image data onto these image sequences. Such processingcould have advantages, especially in terms of complexity and reuse ofexisting hardware implementations, versus performing such processing inthe 3D domain.

One example of such processing is the conversion of the data from an RGB4:4:4 representation to a 4:2:0 YCbCr representation and vice versa. Inthat scenario, for down conversion, the RGB data would be, for example,first converted to a YCbCr 4:4:4 representation, and then the chromaplanes could be filtered and downscaled to ¼ of their originalresolution (half resolution horizontally and vertically). For theinverse process, the chroma planes would be upscaled to their originalresolution, e.g. back to YCbCr 4:4:4, and then the signal would beconverted back to RGB 4:4:4. A variety of down conversion and upconversion methods could be used, including the use of edge adaptivedownscaling and upscaling, as well as techniques such as the lumaadjustment method.

Although some conventional processing methods may operate “as is” on thepoint cloud projected image sequences, they do not fully consider thecharacteristics of such images and in particular the relationship thatexists between different layers or the information about patches andoccupancy. Consideration of such information could improve performance.For example, such methods may be improved by taking into account suchcharacteristics and information, therefore improving performance and thefinal quality of the reconstructed 3D point cloud from the projectedimages.

In particular, conventional methods will most likely process theprojected image sequences assuming that all samples inside thesesequences are highly correlated and that adjoining samples likelycorrespond to the same or at least neighboring objects. Unfortunately,this may not be the case in such imagery. In fact, such image framescomprise samples that correspond to projected patches, as well as fillerareas used to separate and distinguish these patches. Such filler areasmay be left unfilled, e.g. with a default color value, or may have beenfilled using padding methods as described herein. Processing ofindividual samples, e.g. when using long filters for filtering, mayresult in contamination of information between different patches as wellas the filler areas, which can impair quality.

Knowledge of the precise patch location and the filler areas can insteadbenefit performance substantially. In some embodiments, a processingengine (such as texture processing/filtering element 612 and/or geometryprocessing/filtering element 614) performs filtering/processingoperations on such image data on a patch by patch basis. That is, aparticular sample s is processed/filtered by accounting for samples thatcorrespond to the same patch as s. Samples that may have been includedin the processing using a conventional method, e.g. because of theconsideration of a long filter, but which do not correspond to the samepatch are excluded from the processing of s. This could be done, forexample, by reducing the length of the processing filter until suchsamples are fully excluded, or by performing on the fly extrapolation ofthe data at the boundaries between patches, when processing, and usingthe extrapolated data in place of the available data outside a patch.

The same or similar principles could be applied when processing fillerdata, which can be seen as a patch on its own.

A particular system may consider the exact location and shapeinformation for each individual patch, e.g. it may require that thepatch information be fully decoded and therefore is fully availableduring processing. This can provide the most accurate processing and canavoid contamination across patches. In some embodiments, anapproximation of the patch location and shape can be determined bylooking only at the occupancy map information, which may have been codedusing a conventional 2D video coding system. In this case, sinceoccupancy information may have been subsampled, e.g. by 4 timeshorizontally and vertically (16 times overall), some of the samples atthe boundaries of the patch may have been duplicated. This may have someimplications in performance, however, processing complexity can beconsiderably lower since there is no need to decode the full patchinformation.

Point cloud data are associated with geometry information as well asother attributes, e.g. texture, color, reflectance information, etc.Improved performance may be achieved by considering the relationshipsand characteristics across different attributes. In particular,similarity or dissimilarity of the geometry sample values in theprojected plane may be accounted for when processing the correspondingsamples in an attribute plane. In particular, neighboring projectedsamples that correspond to the same or similar depth in the geometryplane are expected to be highly correlated. However, neighboring samplesthat have very dissimilar depth information are less likely to becorrelated. Therefore, when processing such samples, depth informationcould also be considered to determine how these samples should beconsidered.

In some embodiments, samples that are too far from a sample x in termsof depth distance, e.g. exceed a distance threshold T, may be excludedwhen processing sample x. Other samples may be weighted or prioritizedin processing again based on their distance. Corresponding informationfrom other attributes and how similar or dissimilar these attributes arecould also be considered when processing the sample. Information, suchas edges extracted from the geometry plane or from other attributeplanes could also be considered when processing. In the particularexample of chroma down sampling (e.g. 4:4:4 to 4:2:0), as discussedearlier, edge directed downsampling using the geometry as well as lumaattribute information could be performed in the first case, whilesimilarly for up sampling (e.g. 4:2:0 to 4:4:4) an edge directedupsampling process using geometry and luma attribute information couldbe performed. Such processing could again be patch/occupancy map basedas described earlier, however such processing could also be performed onits own without such consideration. In another example, directedinterpolation could be performed of the attribute planes from aresolution H_o×W_o to a new resolution H_n×W_n, again using informationfrom the geometry information and/or other attribute planes that may beavailable. For example, FIG. 6I illustrates an example application wherean attribute plane is upscaled using its corresponding geometryinformation and the geometry extracted edges, according to someembodiments.

In some embodiments, other applications that utilize the proposedfilter/processing techniques described above may include de-noising,de-banding, de-ringing, de-blocking, sharpening, edge enhancement,object extraction/segmentation, display mapping (e.g. for HDRapplications), recoloring/tone mapping, among others. Such methods couldalso be utilized for quality evaluation, e.g. by pooling together andconsidering data (e.g. summing distortion values) in correspondingpatches that also correspond to similar geometry information and otherattributes when evaluating a particular distortion measurement.Processing may be purely spatial, e.g. only projected images thatcorrespond to the same time stamp may be considered for such processing,however temporal/spatio-temporal processing may also be permitted, e.g.using motion compensated or motion adaptive processing strategies.

FIG. 6D illustrates a process for generating video encoded image framesfor patches of a point cloud taking into account relationshipinformation between the patches packed into the image frames, accordingto some embodiments.

At 620, an encoder generates attribute (or texture) image patches forsets of points projected onto patch planes. At 622, the encodergenerates depth (e.g. geometry) patch images for sets of pointsprojected onto the patch planes. At 624, the encoder packs the generatedpatch images into one or more image frames. In some embodiments, variousones of the patch generation and image frame packing techniquesdescribed herein may be used to generate the attribute patch images, thedepth patch images, and to pack the generated patch images into one ormore image frames.

At 626, the encoder generates one or more occupancy maps for the one ormore packed image frames. In some embodiments, various ones of thetechniques described herein to generate an occupancy map may be used.

At 628, the encoder optionally performs one or more pre-processingprocesses on the packed image frames taking into account therelationships between the attribute patch images and the depth patchimages packed into the one or more image frames.

At 630, the encoder (or a separate video encoding component) videoencodes the one or more image frames, wherein one or more parameters ofthe video encoding is adjusted based on the determined relationshipsbetween the patch images. For example, one or more parameters may beadjusted based on a pooled distortion for patches corresponding to thesame set of points. Also, a color conversion process may be adjustedbased on patch boundaries, as a few examples. In some embodiments,various other video encoding parameters may be intelligently adjustedbased on knowledge not typically available to a video encoder regardingwhich portions of an image frame relate to patches, and which do not.For example, as explained above, default encoding algorithms for a videoencoder may assume that pixels proximate to one another are highlycorrelated. However, in the case of packed image frames, points oneither side of a patch image boundary may vary and not be correlated aswould be expected in other video images. As explained above, in someembodiments, a video encoder may disregard padded portions of the imageframe when selecting video encoding parameters. Also, other adjustmentsmay be made in view of knowledge of the relationships between the patchimages packed into the image frames.

FIG. 6E illustrates a process for generating video encoded image framestaking into account pooled distortion for a set of patches correspondingto a same set of points, according to some embodiments.

At 632, an encoder identifies attribute patch images and depth patchimages corresponding to a same set of points projected on a same patchplane. For example, this information may be included in auxiliaryinformation that is to be included in a compressed point cloud stream.

At 634, the encoder determines a pooled amount of distortion affectingthe set of points based on distortion introduced via video encoding theattribute patch images and the depth patch images for the set of points.For example, the encoder may video encode and video decode one or moreimage frames to determine distortion introduced for the respectivepatches. Also, in some embodiments, a video encoding component may haveknown distortion levels associated with various settings and the encodermay be able to determine levels of distortion that will be introducedbased on the selection of such settings. In some embodiments, distortionin one patch image may compound distortion in another patch image whenreconstructing a representation of the point cloud. For example, depthdistortion combined with attribute or texture distortion may compoundupon each other, where points are both moved in a reconstructed pointcloud and also have changed colors. In some embodiments, these effectsmay be greater than the sum of the individual distortions. For example,depth distortion may further highlight color distortion and vice-versa.

At 636, the encoder adjusts one or more video encoding parameters and/orone or more pre-processing parameters to reduce the overall amount ofpooled distortion for the set of points.

FIG. 6F illustrates a process for generating video encoded image framestaking into account patch edges, according to some embodiments.

At 638, an encoder determines edges for attribute patch images or depthpatch images packed into one or more image frames. For example, suchinformation may be included in an occupancy map for the image frame,and/or in auxiliary information for the image frame.

At 640, the encoder provides the relationship information to a videoencoding component that will video encode the image frame.

At 642, the video encoding component adjusts one or more video encodingparameters and/or an image pre-processing/filtering element adjusts oneor more parameters based on the determined edges. For example, apre-processing filter may be adjusted to avoid cross patch contaminationor contamination between patch portions and pad portions of the imageframe.

FIG. 6G illustrates a process for reconstructing a point cloud based onvideo encoded image frames comprising patches of the point cloud,wherein relationship information between the patches packed into theimage frames is taken into account, according to some embodiments.

At 650, a decoder receives one or more image frames comprising attributepatch images and depth patch images packed into one or more imageframes. For example, the one or more image frames may have been packedwith patch images as described herein.

At 652, the decoder receives an occupancy map and/or auxiliaryinformation for the one or more image frames. For example, the one ormore image frames, occupancy map, and/or auxiliary information may beincluded in a group of frames and/or point cloud data stream as shown inFIG. 6C.

At 654, the decoder receives (e.g. via the occupancy map or auxiliaryinformation) or determines (e.g. based on the occupancy map and/orauxiliary information) one or more relationships for the attribute patchimages and/or depth patch images packed in the one or more image frames.In some embodiments, the one or more determined relationships mayindicate portions of the image frame that are occupied and unoccupied.In some embodiments, the one or more determined relationships mayindicate a depth patch image and one or more attribute or texture patchimages that correspond to a same set of points projected on a same patchplane as the depth patch image. In some embodiments, the one or moredetermined relationships may indicate regions of a set of one or moreattribute patch images and one or more depth patch images whichcorrespond to a same depth of the point cloud. In some embodiments, theone or more determined relationships may indicate regions of a set ofone or more attribute patch images which share similar attribute values.In some embodiments, the one or more determined relationships mayindicate regions of a set of one or more attribute patch images and oneor more depth patch images which correspond to patches comprising pointsof the point cloud with depth gradients that deviate from one another byless than a threshold amount. In some embodiments, other relationshipsmay be determined based on the occupancy map and/or auxiliaryinformation.

At 656, the decoder video decodes the one or more packed image frames.In some embodiments, the decoder adjusts decoding parameters based onthe determined relationships.

At 658, the decoder performs one or more post-processing processes onthe video decoded image frames before using the depth patch images andattribute or texture patch images to reconstruct a reconstructedrepresentation of the point cloud. As explained above, it may be moreefficient to perform post-processing on a 2-D representation via a videopost-processing process than to perform post-processing in a 3-D spacecomprising points of a point cloud reconstructed from the patch imagesincluded in the 2-D image frames. The decoder may adjust one or moreparameters of the post-processing based on the determined relationships.For example, a long filter may be adjusted to avoid cross-contaminationacross patch images, between patches and padding, or between patchimages. In some embodiments, various post-processing processes may beadjusted, such as: denoising, debanding, deringing, deblocking,sharpening, object extraction or segmentation, display mapping, whereina range of the one or more image frames is mapped to a range to be usedto display a reconstructed representation of the point cloud, a colorspace conversion of the one or more image frames, a filtering process ofthe one or more image frames, color or tone adjustment processes appliedto the one or more image frames, etc.

At 660, the decoder generates a reconstructed representation of thecompressed point cloud based on attribute information and depthinformation extracted from the patch images in the one or more imageframes that have undergone the post-processing processes at 658.

FIG. 6H illustrates a process of upscaling a patch image included in animage frame taking into account edges of the patch image determinedbased on received or determined relationship information for thepatches, according to some embodiments.

At 670, a decoder determines edges of an attribute patch image or adepth patch image in an image frame based on the determinedrelationships (e.g. determined from an occupancy map and/or auxiliaryinformation). For example, FIG. 6I illustrates an edge extractionprocess being applied to a geometry plane 680 to determine edges 682 ofpatch images in the geometry image frame.

At 670, an attribute patch image or depth patch image is up-scaled. Forexample, any of the up-scaling and down-scaling processes describedherein may take advantage of determined relationships as described foran encoder or decoder in regards to FIGS. 6A-6I. For example, FIG. 6Ialso illustrates an attribute patch image 684 being up-scaled.

At 674, edges of the up-scaled attribute or depth patch image areupdated based on an interpolation between points of the extracted edgesdetermined at 670. For example, FIG. 6I illustrates a directedinterpolation step included in the processing of attribute plane 684 toresult in up-scaled attribute plane 686.

Bit Stream Structure for Compressed Point Cloud Data

As discussed above and in more detail in regard to FIGS. 13 and 14,there is considerable interest in augmented and virtual realityapplications and in the use and compression of 3 dimensional data tosupport them. One such form of data includes point cloudrepresentations, where objects are specified as a series of points thatare described in terms of 3D geometry and a set of attributes per pointthat may include information such as color, reflectance, time, or othertypes of information. Compression of such information is highlydesirable given the amount of space and bandwidth such data wouldrequire if not compressed.

One method that has been proposed for compressing point cloud data isdescribed above in regard to packing patches into video frames and maybe further extended to achieve near lossless or lossless performance byfurther encoding points that are “missed” or not included in thepatches.

The encoder and decoder diagrams as shown in FIGS. 5A and 5B show howthat process is performed. In some embodiments, the point cloud data isfirst segmented into multiple 2D projected images/videos, eachrepresenting different types of information. Segmentation is performedby dividing the point cloud into multiple patches that permit one toefficiently project the entire 3D space data onto 2D planes. Each patchis associated with information such as geometry, texture, and otherattributes if they are available. Such information is then copied at theco-located locations on separate image sequences with each nowcontaining only the geometry information, the texture information, andany other remaining attributes respectively. Auxiliary information thatcontains the patch information as well as an occupancy map that dictateswhich areas in these projected image sequences correspond to actualpoint cloud data and which are unoccupied, e.g. may contain no or dummydata (e.g. padded data), are also provided. Compression is then appliedon such information using different strategies. Auxiliary information,for example, can be entropy coded, while occupancy maps may bedown-converted and encoded using either conventional image/video codecsor other methods such as run length compression. The separate projectedimage sequences may be compressed using conventional codecs. Thisresults in a collection of multiple sub streams, e.g. a geometry substream, texture and attribute sub streams, as well as occupancy andauxiliary information sub streams. All these streams are multiplexedtogether to generate the final point cloud bit stream as shown in thebit stream structure illustrated in FIG. 6C.

In some embodiments, the structure specified in FIG. 6C may be quiterigid and inflexible, and may not account for certain applications,especially low delay applications, that would require all informationcorresponding to a single point cloud frame in time to be efficientlysignaled and decoded within a constrained time frame. The bit streamarchitecture illustrated in FIG. 6C may also impose considerablepenalties in terms of memory and delay. In some embodiments, a pointcloud video sequence is segmented into multiple groups of point cloudframes (GOFs). Group of Frames or GOFs may consist of multiple layers ofinformation, with each one representing different types of data, such asgeometry and texture information among others. In some embodiments, apoint cloud compression PCC decoder is required to first decode andstore the entire geometry video stream for each GOF, as well as anyassociated information with it, followed by the related texture videostream before starting to reconstruct each frame within a GOF (one mayargue that point cloud reconstruction can follow the decoding order ofthe texture video stream). However, the memory requirements may bereduced by scanning the bit stream and finding the appropriate locationof each sub-bit stream (e.g. geometry, occupancy/auxiliary info,texture) and decoding them in parallel. However, this assumes that suchstreams are restricted in using the same coding order and structures.

When all the data is sequentially signaled without any markers toindicate the positions of different sub streams, there may be asignificant disadvantage of time delay. For example, one frame cannot bereconstructed until all the group of frame GOF information is decoded.Also, the bit stream cannot be decoded in parallel unless every data hasinformation of its own size. To resolve this issue, in some embodimentsthe concept of a coding unit, which may be referred to herein as aPCCNAL (Point Cloud Compression Network Abstraction Layer) unit forconvenience, that contains information on one or more types of data andits related header information may be used. Encapsulated data can beplaced in any location within a bit stream and can be decoded andreconstructed in parallel.

In some embodiments, signaling methods of the parameters may not bedefined or limited. The names of the parameters may not be limited aslong as the parameters serve the same purpose. The actual value or codewords of each parameter may not be limited as long as each function ofthe parameter is identified by the numbers.

For example, a bit stream structure for compressed Point Cloud Data thatis more flexible and that permits the delivery of point cloud data forlow delay applications may be used. The bit stream structure may alsoenable other features such as unequal error protection, reordering, andreduced memory footprint, among others. Furthermore, the parametersand/or component units which are used to identify the different methodsand definitions used over the entire slice, frame, GOP, or sequence ofthe Point Cloud Data may also be considered in some embodiments. Anexample of a component unit that is defined and used within a pointcloud compression PCC bit stream is the Point Cloud Compression NetworkAbstraction Layer (PCCNAL) unit. In particular, a PCCNAL unit may bedefined as a set of data that contains one or more types of informationand that can be placed anywhere in the bit stream. However, placementmay be limited within a particular period.

Some other properties of the PCCNAL unit include:

-   -   PCCNAL header: sequence of bits that indicates the start of the        unit and/or the type of the unit. Such a header may contain a        “start code” indicator that is a unique sequence that should not        be present anywhere else within the PCCNAL, and can help in        identifying such a unit. Start code emulation prevention methods        could be used to avoid the presence of equivalent signatures        within the stream.    -   PCCNAL index: index to identify different PCCNAL units    -   PCCNAL size: size of the PCCNAL unit    -   PCCNAL trailing bits: Such information is optional, and similar        to the start code, this is a unique signature that can help in        identifying the end of a PCCNAL unit    -   PCCNAL GoF index: Corresponding GoF index to the PCCNAL units    -   PCCNAL POC: An indexing parameter for such a unit. This index        can be used to classify and/or identify each NAL unit and permit        grouping of different NAL units based on its value. For example,        a geometry and an attribute frame that correspond to the same        Point Cloud frame can be given the same index, which helps        identify their relationship later during decoding and        reconstruction of the point cloud representation. This        information may limit placement of PCCNAL units within the bit        stream.

Each coded block or set of coded blocks can be identified as a PCCNALunit. Such blocks can include sequence parameter sets, picture parametersets, geometry video data, occupancy data, texture video data, geometryframe, occupancy frame and texture frame amongst others. For example,Geometry video stream in FIG. 7A can correspond to geometry video dataPCCNAL(PCCNAL-GEO), auxiliary info & occupancy maps can correspond toPCCNAL-OCC and Texture video stream can correspond to PCCNAL-ATT. In analternative embodiment, all of the geometry video data, occupancy dataand texture video data can comprise one PCCNAL unit.

Examples of PCCNAL unit are as following:

-   -   PCCNAL-SPS: set of parameters used and can be applied over the        entire sequence    -   PCCNAL-PPS: set of parameters used and can be applied over the        entire frame/picture    -   PCCNAL-GOF: set of parameters used and can be applied over the        entire GOF    -   PCCNAL-OCC: set of occupancy map information    -   PCCNAL-GEO: set of geometry data information    -   PCCNAL-ATT: set of texture data information    -   PCCNAL-FRM: information on single frame    -   PCCNAL-GEOFRM: geometry information on single frame    -   PCCNAL-ATTFRM: texture information on single frame    -   PCCNAL-OCCFRM: occupancy map information on single frame

The above information could also be defined for sub-frames, e.g. slices,group of coding tree units (CTUs) or macroblocks, tiles, or groups ofslices or tiles. They can also be specified for a group of frames thatdoes not necessarily need to be equal to the number of frames in a GOF.Such group of frames may be smaller or even larger than a GOF. In thecase that this is smaller, it is expected that all frames inside thisgroup would be a subset of a GOF. If larger, it is expected that thenumber would include several complete GOFs, which might not necessarilybe of equal length. FIG. 7B is an example illustration of the conceptualstructure of PCC encoded bit stream with PCCNAL units

In some embodiments, the PCCNAL units can be signaled sequentiallywithout any marker.

In some embodiments, PCCNAL units can have a PCCNAL header, which mayinclude a start code and/or contain PCCNAL trailing bits. The PCCNALheader is located at the beginning of a PCCNAL unit and the PCCNALtrailing bits are located the end of a PCCNAL unit. By having a PCCNALheader and/or a PCCNAL trailing bits, the decoder can jump to the pointwhere the proper data is located without decode from the beginning tothe data.

For example, in the PCCNAL header a start code can be included, whichcan help in detecting a PCCNAL unit. A start code is a unique sequenceof bits that should not be used for representing any other data withinsuch a unit. When such start code is detected, it may be known that thefollowing bits would correspond to particular information relating tosuch a unit, including its identification information as well as anyrelated payload that would correspond to such an identifier. Forexample, an identifier equal to 000000, assuming 6 bits for theidentifier, can indicate the PCCNAL is GoF Header Unit, while anidentifier equal to 000001 can indicate that the payload includesGeometry Data information. Other identifiers could correspond tooccupancy information, attributes, and so on and such could be definedby the application or user (e.g. engineer configuring theencoder/decoder). It should be pointed out that although start codes arepresent at the beginning of a particular unit, it might be possible toalso define a “start code” that follows a fixed number of bits or syntaxelements, which may be referred to herein as a “preamble” sequence. Forexample, the preamble sequence may include the unit identifier as wellas the POCNAL POC parameter. If the parameters in the preamble sequenceuse variable arithmetic encoding, encoding them in right to left orderin the bit stream (e.g. the least significant bit of the encodedparameter is written first in the stream and the most significant one iswritten last). This is not necessary, but could still be used for fixedlength parameters.

In some embodiments, a PCCNAL header can contain the size of its ownPCCNAL size instead of PCCNAL header bits.

In some embodiments, a PCCNAL header can have both PCCNAL size andPCCNAL header bits.

In some embodiments, a PCCNAL can have trailing bits to indicate the endof the PCCNAL unit.

In some embodiments, a PCCNAL can have its corresponding GoF index.

In some embodiments, a PCCNAL can have its corresponding POC index.

In some embodiments, a PCCNAL can have its corresponding a typeidentifier.

In some embodiments, with the PCCNAL header, PCCNAL units in a bitstream can be located without any fixed order. For example, in someembodiments PCCNAL units can be placed in any order within a bit stream,within the limitations of the PCCNAL POC. Reordering could still beperformed during decoding or reconstruction using the value of PCCNALPOC. PCCNAL POC could be a periodic number however, and such reorderingshould account for such a characteristic. In some embodiments, PCCNALunits can be grouped by their GoF index. In some embodiments, PCCNALunits can be grouped by their POC as depicted in FIG. 7B. In someembodiments, PCCNAL units can be grouped by their types as depicted inFIG. 7C.

In some embodiments, PCCNAL units can be signaled in different bitstreams. Even when they are signaled separately they can bereconstructed properly by PCCNAL header information such as GoF indexand/or POC.

For example, when an encoded PCC bit stream is received at the decoder,the decoder may start parsing PCCNAL unit headers. Using information inthe headers, a decoder can jump through the bit stream to collect syncedoccupancy-geometry-texture data. If a header has the size of the PCCNALunit, it may jump to the end by the size. If a header only contains astart code, it may read through the bit stream until it encounters a newheader or a trailing bits sequence. The decoder can also analyze thePCCNAL POC information for each PCCNAL, determine which units containthe same information and then group and reorder them. Such process canpermit the compressed point cloud data to be properly decoded and thenreconstructed, e.g. by determining which frame in the geometry andattribute video signals correspond to the same point cloud frame andcould be used for its reconstruction. This is a similar mechanism asused in scalable video codecs where the decoder scans through the bitstream and determines correspondence of base and enhancement layersbased on their POCs.

In an encoded PCC bit stream, there can be several parameters per slice,per frame/picture, per GOP, or per sequence of Point Cloud Data, whichsignal information that permits proper decoding and rendering of thepoint cloud data. The parameters can be present in the bit stream morethan one once and at different locations. For example, a parameter canbe signaled at both the sequence level and at the slice level. In thiscase, the parameter at the lower level can overwrite the one at thehigher level within the level's scope. In another embodiment, theparameter at the lower level can provide additional information that canclarify the characteristics of the parameter at the higher level. Set ofthese parameters can comprise a PCCNAL unit. Some example of suchparameters include the following:

-   -   PCC frame width, PCC frame height: the “nominal” width and the        height of the frame that the PCC data is mapped. The size can be        the same as the size of the output of the video codec. The size        can be different from the size of the output of the video codec.        In this case the outputs can be resized by a method indicated in        the parameter sets or predefined by the user/codec.    -   Resizing type: type of resizing method from decoded video size        to PCC frame size    -   Group of Frames size: the number of frames in one group of        frames can be signaled.    -   Chroma format: Chroma format of the geometry data video and        texture data video can be signaled. If necessary, Chroma format        of occupancy map can be signaled as well. The format can be        signaled once for both video layers or can be signaled        separately for each video layer. Such information could also be        inferred from the video bit stream and does not necessarily need        to be present in the PCCNAL unit again.    -   Input, output bit depth: This syntax defines the bit depth of        input PCC data and output PCC data are signaled.    -   Internal bit depth: This syntax element defines the bit depth of        the data for internal computation in the PCC. During the        internal computation, the input data is adjusted to be within        the range of internal bit depth. Such information could also be        inferred from the video bit stream and does not necessarily need        to be present in the PCCNAL unit again.    -   Type of the video codec: This syntax element defines the video        codec, e.g. AVC, HEVC, AV1 or some other codec, as well as the        corresponding profile and level information, that is used for        encoding the Geometry and Attribute projected video data. Such a        syntax element can be signaled once for both the Geometry and        Attribute video signals, or can be signaled independently for        each video signal. Such information could also be omitted and be        inferred by the characteristics of and information within the        video stream.    -   Layers for each stream        -   Presence of layers: a flag that indicates that there are            more than 1 layers for the geometry data/attribute            data/occupancy map in the bit stream        -   Number of layers: in the case that the layers are more than            1, the number of layers is also signaled. This syntax            element defines the number of layers that the Geometry and            Attributes data video have. Each layer contains information            about the points mapped to a same pixel in a patch but each            one corresponds to a different depths.        -   Minimum number of layers: This is an optional syntax element            that defines the minimum number of layers present in the bit            streams.        -   Each video layer can use a different type of a video codec.            The type of the video codec used for a particular layers can            be signaled.    -   Occupancy map        -   Presence of an occupancy map: a flag that indicates the            presence of occupancy map in the bit stream        -   Coding type of the occupancy map: in case that occupancy map            is present, the type of the coding method used for the            occupancy map is also signaled. For example, the occupancy            map can be coded with a video codec or another method            defined in the specification.            Example Methods of Compressing and Decompressing Point            Clouds

FIG. 8A illustrates a process for compressing attribute and spatialinformation of a point cloud, according to some embodiments.

At 802, a point cloud is received by an encoder. The point cloud may becaptured, for example by one or more sensors, or may be generated, forexample in software.

At 804, compressed point cloud information is determined, using any ofthe techniques described herein or using one more combinations of thetechniques described herein.

At 806, a compressed point cloud is encoded using the compressed pointcloud information determined at 804. The point cloud may be compressedusing any of the techniques described herein.

FIG. 8B illustrates a process for decompressing attribute and spatialinformation of a point cloud, according to some embodiments.

At 803 an encoded point cloud is received. The point cloud may have beenencoded using any of the encoding techniques described herein, such aspatch images packed into an image frame that is then encoded by a videoencoder. In some embodiments, the encoded point cloud may comprise pointcloud projections, such as projections onto a cube, cylinder, sphere,etc. that are then encoded via a video encoder.

At 805, spatial and attribute information for the encoded point cloud isdetermined. For example, a video decoder may be used to decode videoencoded packed images or projects. Spatial information may then bedetermined based on the packed images or projections and combined todetermine spatial information for points of the point cloud. Forexample, depth information for points of a patch may be matched with Xand Y information for the points of the patch to determine spatialinformation for the points of the patch in 3D space. In a similar mannerother attributes, included in patch images such as color attributes,texture attributes, etc. may be matched with corresponding points todetermine attribute values for the points. Also, in the case of multipleprojections, the same point may be identified in more than one of theprojections to determine spatial information for the point in 3D space.

At 807, a decompressed point cloud may be provided to a recipient deviceor module.

FIG. 8C illustrates patch images being generated and packed into animage frame to compress attribute and spatial information of a pointcloud, according to some embodiments.

At 810, patches are determined for portions of point cloud. For examplepatches may be determined as described above. At 825 patch informationfor the patches may be generated and at 826, may be encoded to be sentto a decoder. In some embodiments, encoded patch information may beseparately encoded from one or more image frames comprising packed patchimages.

At 811, a first patch (or next patch is selected). At 812 a color (e.g.attribute) patch image is generated for the points of the point cloudincluded in the patch. At 814, one or more additional attribute images,such as a texture attribute image, are generated for the points of thepoint cloud included in the patch.

At 813, spatial information images are generated for the points of thepoint cloud included in the patch. In some embodiments, to generate thespatial information images, the points of the point cloud are projected,at 815, onto a patch plane perpendicular to a normal vector normal to asurface of the point cloud at the patch location. At 817 a first spatialimage is generated for the patch based on the points being projected onthe patch plane at 815. In addition, depth information for the points ofthe patch relative to the patch plane is determined at 816, and at 818 adepth patch image is generated based on the depth information determinedat 816.

At 819, it is determined whether there are additional patches for whichpatch images are to be generated. If so, the process reverts to 811 forthe next patch. If not, at 820 the patch images for the patches arepacked into one or more image frames. In some embodiments, patch imagesfor respective patches may be packed before patch images are determinedfor other patches. At 821, an occupancy map is generated based on wherethe patch images were placed when being packed into the one or moreimage frames. At 824, the occupancy map is encoded. As discussed above,in some embodiments, the occupancy map may be encoded using anarithmetic encoder, entropy encoder etc. Also, in some embodiments, theoccupancy map may be encoded using a video encoder, wherein theoccupancy map is organized as an additional image frame that correspondswith a patch image frame and that represents portions of the patch imageframe that are occupied with patch images (e.g. occupied pixels) andportions of the patch image frame that are padded (e.g. non-occupiedpixels). Video encoding of an occupancy map is discussed in more detailbelow in FIGS. 12A-12C.

At 822, spaces in the one or more image frames that are not occupied bypatch images are padded. In some embodiments, an occupancy map for apatch image frame may be generated before or after the patch image frameis padded at 822.

At 823, the one or more image frames are video encoded, such as inaccordance with a high efficiency video coding (HEVC) standard. In someembodiments, in which an occupancy map is represented by an occupancymap video image frame, the occupancy map video image frame may be videoencoded at 823.

FIG. 9 illustrates patch images being generated and packed into an imageframe to compress attribute and spatial information of a moving orchanging point cloud, according to some embodiments.

At 930, point cloud information for a previously encoded point cloud isreceived wherein the point cloud information represents a subsequentversion of the previously encoded point cloud. For example, thesubsequent version may be a representation of the point cloud at asubsequent moment in time, wherein the point cloud is moving or changingas time progresses.

At 931, it is determined if any new patches need to be determined forthe point cloud. For example, an object not currently in the previouslyencoded point cloud may have been added to the point cloud. For example,the point cloud may be a point cloud of a road and a ball may haveentered into the road. If there is a need to add a new patch, theoccupancy map is updated at 933 to include the new patch and encoded at934. Also, at 932 patch images are generated for the new patch insimilar manner as described in 812-814. The generated patch images areincluded in packing at 943.

At 935, a first or next patch of the patches generated for the reference(previous) point cloud is selected. At 936, the points of the patch arere-sampled as described herein. At 937 motion vectors for the pointsincluded in the selected patch between the reference point cloud and thecurrent point cloud are determined. At 940 the motion vectors are usedto generate a relative motion patch image. For example, in someembodiments, generating a relative motion patch image may comprise,encoding, at 941, vector motion in different directions using differentimage characteristics, as described herein. At 938 an updated colorpatch image is generated. In some embodiments, the updated color patchimage may encode residual values indicating differences in colors of thepoints of the point cloud included in the patch between the referencepoint cloud and the current point cloud. In a similar manner, at 939,other attribute update patch images may be generated.

At 942, it is determined whether there are additional patches to beevaluated. If so, the process reverts to 935 for the next patch. If not,at 943 the patch images for the patches are packed into one or moreimage frames. In some embodiments, patch images for respective patchesmay be packed before patch images are determined for other patches.

At 944, spaces in the one or more image frames that are not occupied bypatch images are padded.

At 945, the one or more image frames are video encoded, such as inaccordance with a high efficiency video coding (HEVC) standard.

FIG. 10 illustrates a decoder receiving image frames comprising patchimages, patch information, and an occupancy map, and generating adecompressed representation of a point cloud, according to someembodiments.

At 1050, an occupancy map is received by a decoder, at 1051 patchinformation is received by the decoder. In some embodiments theoccupancy map and the patch information may be encoded and the decodermay decode the occupancy map and the patch information (not shown). At1052, the decoder receives one or more encoded video image frames. At1052 the decoder identifies patch images in the one or more encodedvideo image frames and at 1054 the decoder decodes the encoded videoimage frames. In some embodiments, the decoder may utilize the occupancymap and the patch information to identify active and non-active portionsof the one or more encoded video images and may adjust one or moredecoded parameters used to decode the encoded video images based onwhether portions, e.g. blocks, sub-blocks, pixels, etc. comprise activeor non-active information.

At 1055, the decoder determines spatial information and/or attributeinformation for the points of the respective patches and at 1056generates a decompressed representation of the point cloud encoded inthe one or more encoded video images.

In some embodiments, active and non-active portions of an image framemay be indicated by a “mask.” For example, a mask may indicate a portionof an image that is a padding portion or may indicate non-active pointsof a point cloud, such as points that are hidden from view in one ormore viewing angles.

In some embodiments, a “mask” may be encoded along with patch images orprojections. In some embodiments, a “mask” may show “active/available”points and “non-active/non-available” points in space. In someembodiments, a mask may be independent from a texture and a depth patchimage. In some embodiments, a mask may be combined with otherinformation, such as a texture or depth patch image. For example, byindicating that certain values in a signal range correspond to activepoints, e.g. values above 16 and below 235 in an 8 bit image, and thatother values correspond to non-active points, e.g. values below 16 orvalues above 235 in an 8 bit image. In some embodiments, additionalconsiderations may be taken to avoid or reduce contamination betweenactive and non-active regions. For example, it may be necessary to makeuse of lossless or visually lossless coding at the boundaries betweenactive and non-active regions.

In some embodiments, a mask may be utilized in a variety of ways forimproving coding efficiency. For example, a mask may be used withprojection methods such as cylindrical, spherical or multiple projectionas wells as decomposition into patches. In addition, a mask may be usedwith a cubic projection method.

FIG. 11A illustrates an encoder, adjusting encoding based on one or moremasks for a point cloud, according to some embodiments.

At 1162, an encoder receives a point cloud. At 1164, the encodergenerate multiple projections or patch images as described herein, forthe received point cloud. At 1166, the encoder determines or more masks.The masks may be hidden points, padded portions of an image frame,points not viewable from a particular view-point, etc. At 1168, theencoder adjusts one or more encoding parameters based on the masks. Forexample the encoder may adjust a budget allocated to masked portions.Additional adjustments that an encoder may perform are described. At1168, the encoder encodes a compressed point cloud, for example via oneor more video encoded image frames.

FIG. 11B illustrates a decoder, adjusting decoding based on one or moremasks for a point cloud, according to some embodiments.

At 1170, a decoder receives an encoded point cloud. At 1172, the decoderdetermines one or more masks for portions of the encoded point cloud.For example, the encoder may determine portions of image framesrepresenting the compressed point cloud correspond to padding. Or, for aparticular view of the point cloud being rendered by the decoder, thedecoder may determine that one or more points of the compressed pointcloud are not viewable from the particular point of view. In someembodiments, mask information may indicate which points are hidden whenthe point cloud is viewed from particular points of view. At 1174, thedecoder adjusts one or more decoding parameters based on the masks.Adjustments that may be made by a decoder based on active/non-activeregions or points (e.g. masks) are described in more detail below. At1176 the decoder decodes the compressed point cloud.

In some embodiments, a mask may be used when performing motionestimation and mode decision. Commonly distortion is computed for anentire block. However, some blocks may have blocks that contain acombination of texture data as well as empty/nonvisible areas. For theseareas only the textured data are of interest and any distortion in thenon-visible areas may be ignored. Therefore, since commonly whenperforming such processes as motion estimation and mode decision, adistortion computation, such as Sum of Absolute Differences (SAD) or Sumof Square Errors (SSE), is performed, a mask may be used to alter thecomputation to exclude distortion for the non-visible areas. Forexample, for the SAD case, distortion may be computed by computing thesum of absolute differences of only samples in a block that correspondto a visible area in a current image. All other samples may be ignoredduring the computation. In some embodiments, distortion may benormalized at the pixel level thus avoiding having to consider blockswith different number of pixels.

In some embodiments, instead of only considering non-visible samples,samples that are adjacent to non-visible samples, or samples identifiedto correspond to different projections (but are placed when encodingwithin the same coding block) may be assigned different weights. Forexample samples in particular blocks could be considered more importantfor subjective quality, and a lower distortion tolerance may beassigned. In such case, the weighting for those samples may beincreased, thus biasing decisions where the distortion for those samplesis lower. Knowledge also that different samples in the same block of aparticular size M×N during motion estimation or mode decision correspondto different objects, may also help with the determination of the blockpartitioning mode, e.g. the encoder could make an early decision (basedpotentially on a preliminary search) on whether different partitioningcould/should be used.

In some embodiments, masks may be used for rate control and rateallocation. For example, it may be desirable that blocks that correspondto areas that contain both visible and non-visible samples be encoded ata different, and some times higher, quality than blocks that containonly visible samples. This is done so as to avoid leakage betweenvisible and not visible samples and ensure the best quality at thepoint-clouds “boundaries”. Different quality may also be assigned basedon depth information, which may also be available on the encoder.Flatter areas may tolerate much more distortion than areas withconsiderable variance in depth. Control of quality may be performed byadjusting quantization parameters/factors, but also by adjusting otherparameters such as the lagrangian multiplier during mode decision, usingdifferent quantization matrices if available, enabling and/or adjustingquantization thresholding and the size and/or shapes of zonalquantization.

Quantization may also be adjusted according to the projection methodused. If, for example an equirectangular projection method was used toproject the object onto a sphere and then onto a 2D plane, it might bedesirable to increase quantization on the top and bottom boundaries, andslowly decrease it when moving towards the center/equator. This may helpcompensate for some of the differences in resolution allocation whenusing a particular projection method. Different adjustments may also bemade to the different color components, again based on similarassumptions, and in consideration again of the mask information.

Quantization may also be performed while considering whether a sample isa visible or a non-visible sample. For example, if a strategy involvesthe use of dynamic programming/trellis quantization methods fordetermining the value of a quantized coefficient. In such embodiments,an impact in distortion of a quantized coefficient, as well as itsimpact on bitrate at multiple reconstruction points may commonly becomputed. This may be done for all coefficients while considering theirbitrate interactions. Finally a decision may be made for allcoefficients jointly by selecting the quantized values that wouldtogether result in the best rate distortion performance. In someembodiments, the visible and non-visible areas may be considered whencomputing such metrics.

Similar to the motion estimation and mode decision processes, sampleadaptive offset (SAO) techniques also commonly compute the resultingdistortion for each possible mode or SAO value that may be used. Again,the decision may exclude non-visible samples, or prioritize, withdifferent weights samples that are close to non-visible samples orsamples that correspond to areas with considerably varying depth.

In some embodiments, masks may be used in any other coding process thatmay involve a distortion computation.

In some embodiments, masks may be used in preprocessing/prefiltering.For example, samples may be prefiltered based on their proximity tonon-visible samples so as to reduce the possibility of artifacts and/orremove noise that may make encoding more difficult. Any form ofprefiltering, including spatio-temporal filters, may be used.

In some embodiments, prefiltering may be applied to both texture as wellas depth information. Decisions in quantization parameters could also bemade at the picture level (temporally) given the amount ofvisible/non-visible samples and depth variance on different pictures.Such decisions could be quite useful, for example, in a multi-passcoding system where analyze the entire sequence is first analyzed todetermine the complexity and relationship of each frame with otherframes. The coding parameters may then be decided that will be used forthat frame in relationship to all other frames and given an expectedquality or bitrate target. Similar decisions may also be made, not onlyfor quantization parameters, but also for the picture coding types (i.e.I, P, or B), structures (e.g. hierarchical or not coding of N framesthat follows a particular coding order of frames), references to use,weighting parameters, etc.

Encoding and Decoding (Normative Concepts)

Since a mask is likely to be available losslessly or visually losslesslyat the decoder, as well as the depth information, this information mayalso be used at the decoder (and of course at the encoder) to furtherimprove quality.

For example, deblocking and sample adaptive offset (SAO), as well asadaptive loop filtering (ALF) and deringing (in codecs that support suchmechanisms), with exclusion of non-visible samples, samples thatcorrespond to different projections, or samples with very differentdepth characteristics may use masking information. Instead, it may bedesirable to only consider for such filtering methods samples thatcorrespond to the same projection and are not so far from each other(depth wise). This may reduce blockiness and/or other artifacts thatthese methods try to mitigate. Other future types of in-loop postfiltering may also be performed in a similar manner.

As another example, out of loop post filtering withvisible/non-visible/different area segmentation may utilize maskinginformation.

Implicit adjustment of QP quality parameters based on a certainpercentage of visible/non-visible samples within a block may beperformed. This may reduce signaling of coding parameters if suchswitching occurs frequently in a bit stream.

Adjustment of the transform type based on the percentage ofvisible/non-visible samples may be performed, including theconsideration of shape adaptive discrete cosine transforms (DCTtransforms).

Adjustment of overlapped block motion compensation techniques mayutilize masking information, if existing in a codec, to mask awaynon-visible samples. A similar consideration may be performed for blockmotion compensation and/or intra prediction (including an intra blockcopy method). Samples that are considered visible may be considered whenconstructing a prediction signal, including also when interpolating toperform subpixel motion compensation or when performing bi-prediction.Masks from the current picture may be considered, but if desired, boththe masks from the current picture as well as the masks corresponding tothe reference pictures could be considered. Such considerations may bemade adaptive at the encoder, through some form of signaling, i.e. atthe sequence, picture, tile, slice, or even CTU/block level.

In some embodiments, clipping of the final value based on the mask ordepth information may be performed.

In some embodiments, other prediction methods that may exist inside acodec (e.g. in AV1 or the Versatile Video Coding (VVC) standardcurrently being developed by the JVET team in MPEG) may be similarlyadjusted or constrained based on the existence (and amount) of visibleand non-visible points, and points corresponding to differentprojections.

In some embodiments, different control/adjustments may be applied todifferent color components as well as to the depth information.

Methods of Occupancy Map Compression

Arithmetic Compression

FIG. 12A illustrates more detail regarding compression of an occupancymap, according to some embodiments. The steps shown in FIG. 12A may beperformed as part of steps 821 or 933 as described above. In someembodiments, any of the occupancy map compression techniques describedherein may be performed at 821 or 933.

At 1280 a list of candidate patches is determined for each block ormodified block of an occupancy map.

At 1281, the lists of candidate patches for each block are ordered in areverse order as an order in which the patches were packed into theimage frame. For example, the patches may be packed into an image, withlarger patches packed before smaller patches. In contrast, the candidatelist for each block of an occupancy map may include smaller patchesbefore larger patches. At 1282, an arithmetic encoder may be used toencode the patch candidate list for each block. In some embodiments, anentropy encoder may be used. Also, in some embodiments, empty blocks maybe assigned a special value, such as zero, whereas patch candidates maybe assigned numbers corresponding to a patch number, such as 1, 2, 3,etc.

At 1283, for each block sub-blocks are determined according to adetermined precision value. The determined precision value may beencoded with the occupancy map such that a decoder may determine thedetermined precision value used at the encoder.

At 1284, for each block, a binary value (e.g. 0 or 1) is determined foreach sub-block of the block. Full sub-blocks are assigned a differentbinary value than non-full sub-blocks. If all sub-blocks of a block arefull, the block may be assigned a binary “full” value.

At 1285, for each non-full sub-block, a traversal order is determined.For example, any of the example traversal orders shown in FIG. 12B (orother traversal orders) may be determined. A run-length encodingstrategy as described above in regard to occupancy map compression maybe used to encode the binary values for the sub-blocks using thedetermined traversal order.

FIG. 12B illustrates example blocks and traversal patterns forcompressing an occupancy map, according to some embodiments. Thetraversal patterns may be used as described above in regard to occupancymap compression and in FIG. 12A. For example, FIG. 12B illustrates block1286 of an occupancy map that includes multiple sub-blocks 1287.

Video Based Compression

FIG. 12C illustrates more detail regarding compression of an occupancymap, according to some embodiments. The steps shown in FIG. 12C may beperformed as part of steps 821 or 933 as described above. In someembodiments, any of the occupancy map compression techniques describedherein may be performed at 821 or 933.

At 1290, for each patch image frame, such as image frames comprisingdepth information patches or attribute value patches, a correspondingoccupancy map image frame is generated. The occupancy map image framemay have a same frame size as the patch image frame and when placed overthe patch image frame the pixels of the occupancy map image frame maymatch up with corresponding pixels in the patch image frame. Because ofthis, a binary value may be assigned to each of the pixels of theoccupancy map image frames to indicate whether the same correspondingpixel in the patch image frame is an occupied pixel or a padded pixel.In some embodiments, an occupancy map image frame may be smaller thanits corresponding patch image frame. In such embodiments, differentvalues may be assigned to the occupancy map image frame pixel toindicate whether its corresponding pixels in the patch image frame areoccupied or padded. For example, a single occupancy map image framepixel may correspond to multiple patch image frame pixels, wherein afirst value indicates a first combination of occupied and unoccupiedpixels in the patch image frame and another value indicates a differentcombination. As an example, say a single occupancy map pixel correspondsto two patch image map pixels. In such an embodiments, a first value inthe occupancy map for the occupancy map pixel may indicate that both thepatch image pixels are un-occupied, a second value may indicate thatboth are occupied, a third value may indicate that an upper one of thetwo pixels is occupied and the other is un-occupied and a fourth valuemay indicate that a lower one of the two pixels is occupied and theother is un-occupied. For example, each block of the occupancy map maybe a multiple larger than a block of the patch image frame (e.g. theoccupancy map may have a lower resolution than the patch image frame).Different color values (e.g. the 1^(st), 2^(nd), 3^(rd) 4^(th) value,etc.) may represent sub-divisions of the occupancy map that correspondwith multiple pixels of the patch image frame.

At 1292 a list of candidate patches is determined for each block of thepatch image frames.

At 1294, the lists of candidate patches for each block are ordered in areverse order as an order in which the patches were packed into theimage frame. For example, the patches may be packed into an image, withlarger patches packed before smaller patches. In contrast, the candidatelist for each block of the patch image frame may include smaller patchesbefore larger patches.

At 1296, the occupancy map image frames are video encoded.

At 1298, an arithmetic encoder may be used to encode auxiliaryinformation, such as the patch candidate list for each block. In someembodiments, an entropy encoder may be used.

Example Applications Using Point Cloud Encoders and Decoders

FIG. 13 illustrates compressed point clouds being used in a 3-Dtelepresence application, according to some embodiments.

In some embodiments, a sensor, such as sensor 102, an encoder, such asencoder 104 or any of the other encoders described herein, and adecoder, such as decoder 116 or any of the decoders described herein,may be used to communicate point clouds in a 3-D telepresenceapplication. For example, a sensor, such as sensor 102, at 1302 maycapture a 3D image and at 1304, the sensor or a processor associatedwith the sensor may perform a 3D reconstruction based on sensed data togenerate a point cloud.

At 1306, an encoder such as encoder 104 may compress the point cloud andat 1308 the encoder or a post processor may packetize and transmit thecompressed point cloud, via a network 1310. At 1312, the packets may bereceived at a destination location that includes a decoder, such asdecoder 116. The decoder may decompress the point cloud at 1314 and thedecompressed point cloud may be rendered at 1316. In some embodiments a3-D telepresence application may transmit point cloud data in real timesuch that a display at 1316 represents images being observed at 1302.For example, a camera in a canyon may allow a remote user to experiencewalking through a virtual canyon at 1316.

FIG. 14 illustrates compressed point clouds being used in a virtualreality (VR) or augmented reality (AR) application, according to someembodiments.

In some embodiments, point clouds may be generated in software (forexample as opposed to being captured by a sensor). For example, at 1402virtual reality or augmented reality content is produced. The virtualreality or augmented reality content may include point cloud data andnon-point cloud data. For example, a non-point cloud character maytraverse a landscape represented by point clouds, as one example. At1404, the point cloud data may be compressed and at 1406 the compressedpoint cloud data and non-point cloud data may be packetized andtransmitted via a network 1408. For example, the virtual reality oraugmented reality content produced at 1402 may be produced at a remoteserver and communicated to a VR or AR content consumer via network 1408.At 1410, the packets may be received and synchronized at the VR or ARconsumer's device. A decoder operating at the VR or AR consumer's devicemay decompress the compressed point cloud at 1412 and the point cloudand non-point cloud data may be rendered in real time, for example in ahead mounted display of the VR or AR consumer's device. In someembodiments, point cloud data may be generated, compressed,decompressed, and rendered responsive to the VR or AR consumermanipulating the head mounted display to look in different directions.

In some embodiments, point cloud compression as described herein may beused in various other applications, such as geographic informationsystems, sports replay broadcasting, museum displays, autonomousnavigation, etc.

Example Computer System

FIG. 15 illustrates an example computer system 1500 that may implementan encoder or decoder or any other ones of the components describedherein, (e.g., any of the components described above with reference toFIGS. 1-14), in accordance with some embodiments. The computer system1500 may be configured to execute any or all of the embodimentsdescribed above. In different embodiments, computer system 1500 may beany of various types of devices, including, but not limited to, apersonal computer system, desktop computer, laptop, notebook, tablet,slate, pad, or netbook computer, mainframe computer system, handheldcomputer, workstation, network computer, a camera, a set top box, amobile device, a consumer device, video game console, handheld videogame device, application server, storage device, a television, a videorecording device, a peripheral device such as a switch, modem, router,or in general any type of computing or electronic device.

Various embodiments of a point cloud encoder or decoder, as describedherein may be executed in one or more computer systems 1500, which mayinteract with various other devices. Note that any component, action, orfunctionality described above with respect to FIGS. 1-14 may beimplemented on one or more computers configured as computer system 1500of FIG. 15, according to various embodiments. In the illustratedembodiment, computer system 1500 includes one or more processors 1510coupled to a system memory 1520 via an input/output (I/O) interface1530. Computer system 1500 further includes a network interface 1540coupled to I/O interface 1530, and one or more input/output devices1550, such as cursor control device 1560, keyboard 1570, and display(s)1580. In some cases, it is contemplated that embodiments may beimplemented using a single instance of computer system 1500, while inother embodiments multiple such systems, or multiple nodes making upcomputer system 1500, may be configured to host different portions orinstances of embodiments. For example, in one embodiment some elementsmay be implemented via one or more nodes of computer system 1500 thatare distinct from those nodes implementing other elements.

In various embodiments, computer system 1500 may be a uniprocessorsystem including one processor 1510, or a multiprocessor systemincluding several processors 1510 (e.g., two, four, eight, or anothersuitable number). Processors 1510 may be any suitable processor capableof executing instructions. For example, in various embodimentsprocessors 1510 may be general-purpose or embedded processorsimplementing any of a variety of instruction set architectures (ISAs),such as the x86, PowerPC, SPARC, or MIPS ISAs, or any other suitableISA. In multiprocessor systems, each of processors 1510 may commonly,but not necessarily, implement the same ISA.

System memory 1520 may be configured to store point cloud compression orpoint cloud decompression program instructions 1522 and/or sensor dataaccessible by processor 1510. In various embodiments, system memory 1520may be implemented using any suitable memory technology, such as staticrandom access memory (SRAM), synchronous dynamic RAM (SDRAM),nonvolatile/Flash-type memory, or any other type of memory. In theillustrated embodiment, program instructions 1522 may be configured toimplement an image sensor control application incorporating any of thefunctionality described above. In some embodiments, program instructionsand/or data may be received, sent or stored upon different types ofcomputer-accessible media or on similar media separate from systemmemory 1520 or computer system 1500. While computer system 1500 isdescribed as implementing the functionality of functional blocks ofprevious Figures, any of the functionality described herein may beimplemented via such a computer system.

In one embodiment, I/O interface 1530 may be configured to coordinateI/O traffic between processor 1510, system memory 1520, and anyperipheral devices in the device, including network interface 1540 orother peripheral interfaces, such as input/output devices 1550. In someembodiments, I/O interface 1530 may perform any necessary protocol,timing or other data transformations to convert data signals from onecomponent (e.g., system memory 1520) into a format suitable for use byanother component (e.g., processor 1510). In some embodiments, I/Ointerface 1530 may include support for devices attached through varioustypes of peripheral buses, such as a variant of the Peripheral ComponentInterconnect (PCI) bus standard or the Universal Serial Bus (USB)standard, for example. In some embodiments, the function of I/Ointerface 1530 may be split into two or more separate components, suchas a north bridge and a south bridge, for example. Also, in someembodiments some or all of the functionality of I/O interface 1530, suchas an interface to system memory 1520, may be incorporated directly intoprocessor 1510.

Network interface 1540 may be configured to allow data to be exchangedbetween computer system 1500 and other devices attached to a network1585 (e.g., carrier or agent devices) or between nodes of computersystem 1500. Network 1585 may in various embodiments include one or morenetworks including but not limited to Local Area Networks (LANs) (e.g.,an Ethernet or corporate network), Wide Area Networks (WANs) (e.g., theInternet), wireless data networks, some other electronic data network,or some combination thereof. In various embodiments, network interface1540 may support communication via wired or wireless general datanetworks, such as any suitable type of Ethernet network, for example;via telecommunications/telephony networks such as analog voice networksor digital fiber communications networks; via storage area networks suchas Fibre Channel SANs, or via any other suitable type of network and/orprotocol.

Input/output devices 1550 may, in some embodiments, include one or moredisplay terminals, keyboards, keypads, touchpads, scanning devices,voice or optical recognition devices, or any other devices suitable forentering or accessing data by one or more computer systems 1500.Multiple input/output devices 1550 may be present in computer system1500 or may be distributed on various nodes of computer system 1500. Insome embodiments, similar input/output devices may be separate fromcomputer system 1500 and may interact with one or more nodes of computersystem 1500 through a wired or wireless connection, such as over networkinterface 1540.

As shown in FIG. 15, memory 1520 may include program instructions 1522,which may be processor-executable to implement any element or actiondescribed above. In one embodiment, the program instructions mayimplement the methods described above. In other embodiments, differentelements and data may be included. Note that data may include any dataor information described above.

Those skilled in the art will appreciate that computer system 1500 ismerely illustrative and is not intended to limit the scope ofembodiments. In particular, the computer system and devices may includeany combination of hardware or software that can perform the indicatedfunctions, including computers, network devices, Internet appliances,PDAs, wireless phones, pagers, etc. Computer system 1500 may also beconnected to other devices that are not illustrated, or instead mayoperate as a stand-alone system. In addition, the functionality providedby the illustrated components may in some embodiments be combined infewer components or distributed in additional components. Similarly, insome embodiments, the functionality of some of the illustratedcomponents may not be provided and/or other additional functionality maybe available.

Those skilled in the art will also appreciate that, while various itemsare illustrated as being stored in memory or on storage while beingused, these items or portions of them may be transferred between memoryand other storage devices for purposes of memory management and dataintegrity. Alternatively, in other embodiments some or all of thesoftware components may execute in memory on another device andcommunicate with the illustrated computer system via inter-computercommunication. Some or all of the system components or data structuresmay also be stored (e.g., as instructions or structured data) on acomputer-accessible medium or a portable article to be read by anappropriate drive, various examples of which are described above. Insome embodiments, instructions stored on a computer-accessible mediumseparate from computer system 1500 may be transmitted to computer system1500 via transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as a network and/or a wireless link. Various embodiments mayfurther include receiving, sending or storing instructions and/or dataimplemented in accordance with the foregoing description upon acomputer-accessible medium. Generally speaking, a computer-accessiblemedium may include a non-transitory, computer-readable storage medium ormemory medium such as magnetic or optical media, e.g., disk orDVD/CD-ROM, volatile or non-volatile media such as RAM (e.g. SDRAM, DDR,RDRAM, SRAM, etc.), ROM, etc. In some embodiments, a computer-accessiblemedium may include transmission media or signals such as electrical,electromagnetic, or digital signals, conveyed via a communication mediumsuch as network and/or a wireless link.

The methods described herein may be implemented in software, hardware,or a combination thereof, in different embodiments. In addition, theorder of the blocks of the methods may be changed, and various elementsmay be added, reordered, combined, omitted, modified, etc. Variousmodifications and changes may be made as would be obvious to a personskilled in the art having the benefit of this disclosure. The variousembodiments described herein are meant to be illustrative and notlimiting. Many variations, modifications, additions, and improvementsare possible. Accordingly, plural instances may be provided forcomponents described herein as a single instance. Boundaries betweenvarious components, operations and data stores are somewhat arbitrary,and particular operations are illustrated in the context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within the scope of claims that follow. Finally,structures and functionality presented as discrete components in theexample configurations may be implemented as a combined structure orcomponent. These and other variations, modifications, additions, andimprovements may fall within the scope of embodiments as defined in theclaims that follow.

What is claimed is:
 1. A system, comprising: an encoder configured tocompress a point cloud, wherein to compress the point cloud, the encoderis configured to, for respective ones of a plurality of respective setsof points of the point cloud: generate, for the respective ones of therespective sets of points, respective attribute patch images comprisingattribute information for the respective sets of points projected ontorespective patch planes; generate, for the respective ones of therespective sets of points, respective depth patch images comprisingdepth information for the respective sets of points projected onto therespective patch planes; pack the generated attribute patch images andthe generated depth patch images in one or more image frames; provide,to a video encoding component of the encoder, relationship informationindicating relationships between the respective attribute patch imagesor the respective depth patch images, and video encode, via the videoencoding component, the one or more image frames, wherein the videoencoding component is configured to adjust one or more parameters usedto video encode the one or more image frames based on the providedrelationship information.
 2. The system of claim 1, wherein the encoderis further configured to: encode, via the video encoding component oranother encoding component of the encoder, an occupancy map indicatingportions of the one or more image frames that are occupied with patchimages and portions of the one or more image frames that are unoccupied,wherein the relationship information provided to the video encodingcomponent comprises occupancy information determined based, at least inpart, on the occupancy map.
 3. The system of claim 1, wherein theoccupancy information included in the relationship information providedto the video encoding component, causes the video encoding component to:allocate more encoding resources to encode the portions of the one ormore image frames that are occupied with patch images; and allocatefewer encoding resources to encode the portions of the one or more imageframes that are unoccupied.
 4. The system of claim 1, wherein therelationship information indicates patch images in the one or more imageframes which correspond to a same set of points projected on a samepatch plane.
 5. The system of claim 4, wherein the encoder is configuredto: determine a level of distortion for the set of points projected onthe same patch plane based on pooling distortions introduced, via thevideo encoding, in a plurality of the patch images which correspond tothe same set of points in the one or more image frames; and adjust oneor more encoding parameters of the video encoding component, based atleast in part, on the determined level of distortion for the set ofpoints.
 6. The system of claim 1, wherein the encoder is configured to:determine edges of the attribute patch images or the depth patch imagespacked in the one or more image frames; and include determined edgeinformation for the attribute patch images or the depth patch images inthe relationship information provided to the video encoding component;wherein the video encoding component is configured to: adjust colordown-sampling of the one or more image frames based on the edgeinformation.
 7. A system, comprising: a decoder configured to: receiveone or more video encoded image frames comprising attribute patch imagesand depth patch images packed into one or more image frames that havebeen video encoded; receive an occupancy map for the one or more imageframes; video decode the one or more video encoded image frames; performone or more post-processing processes on decoded ones of the one or morevideo encoded image frames; and reconstruct a representation of thepoint cloud based on the one or more post-processed video decoded imageframes, wherein to perform the post processing the decoder is configuredto: adjust one or more parameters of the one or more post processingprocesses based on received or determined relationship informationindicating relationships between the respective attribute patch imagesor the respective depth patch images.
 8. The system of claim 7, whereinthe received or determined relationship information comprises anidentification of portions of the one or more image frames thatcorrespond to attribute patch images or depth patch images and one ormore portions of the one or more image frames that are unoccupied, andwherein the decoder is configured to allocate fewer post-processingresources to the unoccupied portions of the one or more image framesthan are allocated to the portions of the one or more image frames thatcorrespond to the attribute patch images or the depth patch images. 9.The system of claim 7, wherein the determined relationship informationcomprises information indicating a set of one or more attribute patchimages and a depth patch image that correspond to a same set of pointsprojected on a same patch plane.
 10. The system of claim 7, wherein thedetermined relationship information comprises information indicatingregions of a set of one or more attribute patch images and one or moredepth patch images which correspond to a same depth of the point cloud.11. The system of claim 7, wherein the determined relationshipinformation comprises information indicating regions of a set of one ormore attribute patch images which share similar attribute values. 12.The system of claim 7, wherein the one or more post-processing processescomprises one or more of: denoising; debanding; deringing; deblocking;sharpening; object extraction or segmentation; display mapping, whereina range of the one or more image frames is mapped to a range to be usedto display a reconstructed representation of the point cloud; a colorspace conversion of the one or more image frames; a filtering process ofthe one or more image frames; or color or tone adjustment processesapplied to the one or more image frames.
 13. The system of claim 7,wherein the one or more post-processing processes comprises: determiningedges of the attribute patch images or the depth patch images of the oneor more image frames; upscaling the one or more image frames; andadjusting up-scaled edges of the patch images or the depth images inup-scaled versions of the one or more image frames based on aninterpolation of the determined edges prior to the upscaling.
 14. Amethod of reconstructing a point cloud, comprising: receiving one ormore image frames comprising attribute patch images and depth patchimages packed into the one or more image frames; receiving an occupancymap for the one or more image frames; performing one or morepost-processing processes on the one or more image frames, wherein saidperforming the one or more post-processing processes comprises:determining or receiving relationship information indicatingrelationships between the respective attribute patch images and therespective depth patch images, wherein the relationship information isdetermined based on the occupancy map, the attribute patch images, orthe depth patch images; and adjusting one or more parameters of the oneor more post processing processes based on the determined or receivedrelationship information; and reconstructing a representation of thepoint cloud based on the one or more post-processed image frames. 15.The method of claim 14, wherein said determining the relationshipinformation comprises: determining portions of the one or more imageframes that correspond to attribute patch images or depth patch images;and determining one or more portions of the one or more image framesthat are unoccupied.
 16. The method of claim 14, wherein saiddetermining the relationship information comprises: determining a set ofone or more attribute patch images and a depth patch image thatcorrespond to a same set of points projected on a particular patchplane.
 17. The method of claim 14, wherein said determining therelationship information comprises: determining regions of a set of oneor more attribute patch images and one or more depth patch images whichcorrespond to a same depth of the point cloud.
 18. The method of claim14, wherein said determining the relationship information comprises:determining regions of a set of one or more attribute patch images thathave common attribute values that deviate from one another by less thana threshold amount.
 19. The method of claim 14, wherein said determiningthe relationship information comprises: determining regions of a set ofone or more attribute patch images and one or more depth patch imageswhich correspond to patches comprising points of the point cloud withdepth gradients that deviate from one another by less than a thresholdamount.
 20. The method of claim 14, wherein said adjusting one or moreparameters of the one or more post processing processes based on thedetermined or received relationship information comprises: adjusting oneor more parameters of a noise reduction algorithm; adjusting one or moreparameters of a debanding filter; adjusting one or more parameters of aderinging filter; or adjusting one or more parameters of a deblockingfilter.